Analysis of the effect of wind speed in increasing the COVID-19 cases in Jakarta

Dewi Susanna; Yoerdy Agusmal Saputra; Sandeep Poddar

doi:10.12688/f1000research.128908.1

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Research Article

Analysis of the effect of wind speed in increasing the COVID-19 cases in Jakarta

[version 1; peer review: 1 approved with reservations]

Dewi Susanna ¹, Yoerdy Agusmal Saputra², Sandeep Poddar³

PUBLISHED 08 Feb 2023

Author details Author details

¹ Department of Environmental Health, Faculty of Public Health, Universitas Indonesia, Depok, Jawa Barat, 16424, Indonesia
² Study Program of Public Health, Institut Kesehatan Indonesia, Jakarta Utara, Jakarta, 14240, Indonesia
³ Research and Innovation, Lincoln University College, Petaling Jaya, Selangor, 47301, Malaysia

Dewi Susanna
Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Validation, Visualization, Writing – Review & Editing

Yoerdy Agusmal Saputra
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Visualization, Writing – Original Draft Preparation

Sandeep Poddar
Roles: Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Emerging Diseases and Outbreaks gateway.

Abstract

Background: COVID-19 remains a public health problem around the world. It is possible the climate could affect the transmission of COVID-19. Wind is one of the climate factors besides temperature, humidity, and rainfall. This study aimed to describe spatial patterns and find the correlation of wind speed (maximum and average) with the pattern of COVID-19 cases in Jakarta, Indonesia.
Methods: The design of this study was an ecological study based on time and place to integrate geographic information systems and tested using statistical techniques. The data used were wind speed and weekly COVID-19 cases from March to September 2020. These records were obtained from the special coronavirus website of Jakarta Provincial Health Office and the Indonesian Meteorology, Climatology and Geophysics Agency. The data were analyzed by correlation, graphic/time trend, and spatial analysis.
Results: The wind speed (maximum and mean) from March to September 2020 tended to fluctuate between 1.43 and 6.07 m/s. The correlation test results between the average wind speed and COVID-19 cases in Jakarta showed a strong positive correlation (r = 0.542; p value = 0.002).
Conclusions: The spatial overlay map of wind speed (maximum and mean) with COVID-19 cases showed that villages with high wind speeds, especially coastal areas, tended to show an earlier increase in cases. The higher wind speed allowed an increase in the distribution of the COVID-19 virus in the air in people who did not apply health protocols properly.

Keywords

COVID-19, SAR-CoV-2, maximum wind speed, average wind speed, spatial–temporal analysis

Corresponding author: Dewi Susanna

Competing interests: No competing interests were disclosed.

Grant information: This research was funded by the Directorate Research and Development Universitas Indonesia through Nota Dinas No: PENG-006/UN2.RST/PPM.00.00/2022 and Contract No. NKB-1283/UN2.RST/HKP.05.00/2022 International Publication Q2 Grant year 2022–2023
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2023 Susanna D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Susanna D, Saputra YA and Poddar S. Analysis of the effect of wind speed in increasing the COVID-19 cases in Jakarta [version 1; peer review: 1 approved with reservations]. F1000Research 2023, 12:145 (https://doi.org/10.12688/f1000research.128908.1) First published: 08 Feb 2023, 12:145 (https://doi.org/10.12688/f1000research.128908.1) Latest published: 28 Dec 2023, 12:145 (https://doi.org/10.12688/f1000research.128908.3)

Introduction

Several nations are currently experiencing a significant increase in coronavirus (COVID-19) cases, including Indonesia (https://worldometers.info/coronavirus/). A total of 34,874,744 confirmed cases with 1,097,497 deaths (case fatality rate (CFR) 3.1%) were reported in 216 countries based on data from the World Health Organization (https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200831-weekly-epi-update-3.pdf?sfvrsn=d7032a2a_4). In Indonesia, the number of people who have been infected and the number who have died are approximately 287,008 and 10,740 (CFR 3.7%), respectively, with the most predominant regions being Jakarta (73,700), East Java (43,536), and Central Java (22,440) (https://covid19.go.id/peta-sebaran).

Based on various studies worldwide, the SARS-CoV-2 virus that is responsible for COVID-19 is described as highly contagious. Saliva droplets produced by asymptomatic carriers appear to be the possible transmission media. In addition, specific observations using highly sensitive laser beams indicated that loud speech tends to emit countless droplets of oral fluid per second. In a closed and stagnant air environment, the drops finally disappear from the viewing window within the range of eight to14 minutes. Based on these findings, regular speech shows a high probability of transmitting the airborne virus in confined spaces.¹ Therefore, aerosol transfer appears to be the most significant method of SARS-CoV-2 spread compared with other media.¹^–³ Furthermore, recent studies have shown that the virus remains active in airborne particles beyond three hours.⁴^,⁵

Although related studies are minimal, the wind is perceived as a critical climatic factor for virus transmission.⁶ Previous research studied four meteorological parameters (temperature, dew point, humidity, and wind speed). The number of coronavirus cases in Turkey demonstrated the function of wind speed in promoting the spread. The parameter with the highest correlation was generated by wind speed in 14 days and therefore showed that extensive wind speeds led to increased virus cases.⁷

Conversely, higher wind speeds in external settings contribute to dilution and droplet removal, resulting in a decline in airborne concentration (https://apps.who.int/iris/bitstream/handle/10665/70863/WHO_CDS_CSR_GAR_2003.11_eng.pdf?sequence=1&isAllowed=y).⁸ Previous research from Brazil that examined the association between weather and COVID-19 spread in tropical climates countries showed that wind speed was negatively correlated (p < 0.01). Therefore, the variable also serves as a potential consideration in suppressing disease transmission.⁹

Based on the description above, it can be assumed that wind speed is a major influencing factor of COVID-19 spread. Therefore, it was crucial to comprehend the effects of wind speed on the virus, as Jakarta appeared to be the pandemic's epicenter. Consequently, this research successfully investigated the relationship between wind speed and the weekly occurrence of COVID-19 in the Special Capital Region of Jakarta.

Methods

A step by step description of the protocols can be obtained at: dx.doi.org/10.17504/protocols.io.ewov1odzylr2/v1

Study design

A quantitative method was applied with an ecological design that provides real-time and location analysis, using geographic information systems and the data were tested using statistical techniques.

Data collection

The secondary data comprised daily reports of COVID-19 infection and wind speed (maximum and mean) in Jakarta from the pandemic inception, specifically between March and September 2020. These records were obtained from the website of the Jakarta Provincial Health Office (https://corona.jakarta.go.id/id/data-pemantauan) and the website of the Indonesian Meteorology, Climatology and Geophysics Agency (https://dataonline.bmkg.go.id/akses_data). Subsequently, the general information was converted into 31-weeks documentation. Furthermore, a basic map of Jakarta with neighboring community boundaries was obtained using the GADM Map and Data site (https://gadm.org/maps/IDN/jakartaraya.html). Coordinates for the weather monitoring stations were accessed online (https://www.gps-latitude-longitude.com/). The Jakarta province comprising 261 urban villages served as the research location.

Statistics data analysis

Univariate analysis was conducted to determine individual variable distribution, including maximum and average wind speed (m/s), as well as the number of COVID-19 cases. This process is descriptive and quantitative, where the data exist in statistical distribution tables, line graphs, and thematic maps based on the research objectives. Subsequently, the bivariate analysis involved Pearson's product-moment correlation test to evaluate the relationship between independent (wind speed factor) and dependent variables (COVID-19). Specifically, the method stated the possible existence (p < 0.05), closeness (r), and direction of the relationship. In addition, the strengths of the association were qualitatively divided into four categories, where r = 0.00–0.25 was absence/weak relationship, r = 0.26–0.50 was moderate, r = 0.51–0.75 was strong and r = 0.76–1.00 was very strong/perfect.(10) The correlation value also determined the direction of the relationship as a positive (+) or negative (−) pattern. This value (r) was evaluated by the conditions, where r = 0 was no linear relationship, r = −1 was perfect negative linear and r = 1 was perfect positive linear.¹⁰ Furthermore, the univariate and bivariate analyses were conducted at the Computer Laboratory using SPSS 21 software (RRID:SCR_002865).

Spatial data analysis

Spatial analysis was performed to observe the relationship pattern between the two variables. Based on a selected community, an interpolation process was employed to create an overlay map of COVID-19 cases and climate parameters. The Jakarta grid map interpolation was used to estimate the magnitude of climate variables outside the measurement points (weather stations) by applying the following steps. Firstly, a grid map of five weather monitoring stations was created. The interpolation was performed by entering the point values or coordinate attribute data (longitude and latitude) into the climate variable attribute table. The coordinate points were joined in the climate variable map. Secondly, the independent variable vector data were digitized by inputting the spatial data on climate variables into a base map, then processing and selecting a color symbol (singleband pseudocolor) with color ramp blues. Consequently, a digital category of high and low climate variables was formed depending on the data magnitude. Thirdly, the dependent variable vector data were digitized by entering spatial data on COVID-19 rates into the base map, depending on the community, followed by processing and selecting a point symbol (centroid). A digital category of large and small cases was generated based on the disease data. Fourthly, the two vector maps were interpolated with the plugin interpolation menu. Therefore, an interpolated raster plot was obtained and used to analyze or predict the climate variable values in each community. The resulting color gradations and point symbols did not show any ratio but only reported ordinal values, including high-low climate variations and number of virus cases. This color gradation ranged from dark blue to white, indicating high to low wind speeds (maximum and mean). Subsequently, the colors were created digitally using a singleband pseudocolor with ramp blues colors from Quantum Geographic Information System (QGIS) software (RRID:SCR_018507) with a natural grouping of five classes, where very dark blue = very high, dark blue = high, blue = medium, light blue = and white = very low. The dot symbol (centroid) varied from large to small, representing the virus spread. Similarly, the point symbol size was digitally generated using a simple marker or a standard symbol from the QGIS software with a linear classification between 0 and 17.

The spatially analyzed data were further processed with overlayed thematic graphics and maps to show the relationship pattern based on time and location. The spatial analysis was associated with the statistical correlation results generated using the QGIS software version 3.0 at the Computer Laboratory of the Faculty of Public Health, University of Indonesia.

Results

The wind speed (maximum and mean) from March to September 2020 tended to fluctuate between 1.43 and 6.07 m/s. This variable appeared minimal during the last week of each month (fourth, fifth, fourth, fourth, and fifth weeks of March, April, May, June, and July, respectively). However, it showed a higher performance in the middle of each month (second, second, second, third, third, and third weeks of March, May, June, July, August, and September, respectively).

Furthermore, the maximum and minimum wind speed values of 6.07 and 4.5m/s occurred during the first, ninth, and 15^th weeks, respectively. Meanwhile, the optimal and minimum average wind speeds of 2.75 and 1.43 m/s were obtained in the 29^th and fourth weeks, respectively.

Statistical relationship analysis

The Pearson product-moment correlation test was applied in the statistical analysis of the relationship to show the occurrence (p < 0.05), direction (positive/direct or negative/opposite), and closeness (r). The Jakarta data from March to September 2020 were processed. The correlation test results between the average wind speed and COVID-19 cases showed a strong relationship strength and a positive pattern (p = 0.002, r = 0.542). These conditions indicated that higher average wind speeds increased the possibility of COVID-19 occurrence. However, no significant relationship existed between the maximum wind speed and the virus occurrence.

Graphical relationship analysis/time trend

Figure 1 shows that wind speeds possibly generated a pattern similar to the COVID-19 cases. The reverse form of the maximum wind speed with COVID-19 cases occurred in the second to fourth, eighth, 15th, 17th, 19th, 22nd, 23rd, 27th, 28th, 30th, and 31st weeks, while for average wind speed, the pattern was observed at weeks three, four, seven, nine, 13, 14, 16, 17, 19–22 and 27–29.

Figure 1. Wind speed (maximum and average) and COVID-19 cases per week in the Special Capital Region of Jakarta (accessed from: https://dataonline.bmkg.go.id/akses_data and https://corona.jakarta.go.id/id/data-pemantauan).

Spatial relationship analysis

The spatial analysis was created by overlaying a map of COVID-19 cases with a plot of the sunlight duration to obtain the interpolation from the weather monitoring stations. Color gradations and dot symbols represent this interval and the virus occurrence. In addition, the interpolated values were used to predict a relationship between sunlight duration and COVID-19 in each village around the research location.

Apart from generating a high-low comparison of sun exposure to the number of COVID-19 cases, the virus transmission pattern from March to September 2020 was also ascertained. There were incomplete data, including the records per village, that commenced from March 25, 2020. This circumstance tended to influence the distribution pattern in March, causing a uniform condition to be a low category.

In each month, the spatial pattern of maximum wind speed tended to fluctuate, including the maximum high wind speed dominating the central and northern parts of the capital city in the first two months (March and April). Subsequently, the flow expanded to the northeast and northwest regions, comprising Tanjung Priok, Koja, Kebon Bawang, Lagoa, Kali Baru, North Rawabadak, South Rawabadak, Warakas, Papanggo, North Tugu, West Semper, Cilincing, and East Semper communities. The maximum moderate wind speed consistently occurred in the southeastern part that included most of the urban villages in East Jakarta. In contrast, low maximum wind speeds were reported in the southwest part relating to the urban villages in South Jakarta.

Based on the spatial map overlaying the maximum wind speed and COVID-19 cases, urban villages with high maximum wind speeds tended to demonstrate faster virus transmission than areas with moderate and low maximum wind speeds. A relatively high spike in cases in July circulated across the villages of Lagoa (101) and Kebon Bawang (10), and in August at Lagoa (144), Cilincing (131), South Rawabadak (105), and West Semper (109), compared with other neighboring villages. However, a small number of villages with moderate and low maximum wind speeds also showed an increase in July at West Cempaka Putih (101), and in August at Johar Baru village (127), and West Cempaka Putih (106), as illustrated in Figure 2.

Figure 2. Maximum wind speed spatial pattern with COVID-19 cases from March to September 2020 (accessed from: https://dataonline.bmkg.go.id/akses_data and https://riwayat-file-covid-19-dki-jakarta-jakartagis.hub.arcgis.com/).

The spatial pattern of the average wind speed in each month appeared relatively similar, including the high average wind speed dominating the northeast and northwest regions of the city. This area covered Kebon Bawang, Tanjung Priok, Koja, Lagoa, Kali Baru, North Rawabadak, South Rawabadak, Warakas, Papanggo, North Tugu, West Semper, Cilincing, Kramat, Tegal Alur, Pengadungan, Kali Deres, and Samanan. Furthermore, the average wind speed consistently occurred in the areas with high and low average wind speed convergence, including Kapuk Muara, Kapuk, Rawa Buaya, Duri Kosambi, Sunter Agung, Sunter Jaya, West Kelapa Gading, East Kelapa Gading, and Pegangsaan Dua. The low average wind speeds also remained constant in the central and southern parts, including central, south, and east Jakarta.

Based on the spatial overlay map of the average wind speed and COVID-19 cases, villages with high average wind speeds experienced an increase of cases faster compared with areas with medium and low average wind speeds. A relatively high spike also occurred in July at Kebon Bawang (101) and Lagoa (101), and in August at Lagoa (144), Cilincing (131), West Semper (109), and South Rawabadak (105), compared with other adjacent villages. However, a smaller number of villages with medium and low average wind speeds also showed an increase in July at West Cempaka Putih (101), and in August at West Pademangan (158), Johar Baru (127), and West Cempaka Putih (106), as represented in Figure 3.

Figure 3. Spatial pattern of average wind speed with COVID-19 cases from March to September 2020 (accessed from: https://dataonline.bmkg.go.id/akses_data and https://riwayat-file-covid-19-dki-jakarta-jakartagis.hub.arcgis.com/).

Discussion

Based on the correlation analysis between the average wind speed and COVID-19 cases in Jakarta, a significant correction between strong and unidirectional associations was reported, indicating that higher average wind speeds accelerated the virus transmission. This outcome matched several previous studies,⁷^,¹¹^–¹⁶ where the wind speed was also known to increase the airborne SARS-CoV-2 spread.¹² Droplets released during normal speech tend to survive in the air between eight and 14 minutes, similar to confined environments.¹ Recent studies have also shown that the virus can remain infectious in airborne particles beyond three hours.⁴^,⁵ Therefore, aerosol transmission appears to be the main channel, compared with other media.¹^–³ Under these circumstances, the wind speed variable plays a significant role in promoting the spread,⁷ and with wind speeds in Jakarta ranging from 1.43 to 6.07 m/s, the surviving airborne virus quickly spreads.¹²

In this context, higher wind speeds adversely impact individuals who do not correctly apply health protocols (avoiding crowds, not keeping close distances, and not wearing/removing masks).⁷ Therefore, the transmission of impure cases is influenced by the rate of wind speed spreading the virus. Furthermore, government policies also play an essential role in influencing the disease rates, in the form of implementing health protocols (washing hands,¹⁷ wearing masks,¹⁷ physical distancing¹⁸), staying at home,¹⁹ working from home,²⁰^,²¹ and large-scale social restrictions/enforcement of restrictions on community activities (Pembatasan Sosial Berskala Besar/Pemberlakuan Pembatasan Kegiatan Masyarakat).²²^–²⁴

Based on a weekly graphical analysis of Jakarta, the fluctuations in wind speed tended towards the pattern of COVID-19 cases. The variable results were also consistent with similar outcomes of the correlation test in Jakarta, indicating a significant relationship with strong and positive patterns. This also indicated that higher average wind speeds triggered extensive COVID-19 spread.

The spatial relationship between wind speed and COVID-19 cases in Jakarta from March to September 2020 showed that urban villages that had high wind speeds tended to experience a faster transmission than other areas with medium and low values. However, a smaller number of villages with moderate and low wind speeds also showed increased cases. This result was possibly influenced by determining the source location of the COVID-19 cases using the domicile/residential address in the last 14 days, despite the possible occurrence of the infection while conducting external activities. Therefore, the spatial pattern of COVID-19 cases in Jakarta did not match the actual spatial pattern from the infection origin.²⁵ In addition, other factors, including community non-compliance in implementing health protocols and Pembatasan Sosial Berskala Besar (PSBB) or large-scale social restrictions policies, led to transmissions outside the home (https://www.liputan6.com/news/read/4368373/survei-bps-55-persen-masyarakattak-patuhi-protokol-kesehatan-karena-tidak-ada-sanksi). The previous survey proved that approximately 26.46% of the community did not correctly implement health protocols outside the home (https://www.bps.go.id/publication/2020/09/28/f376dc33cfcdeec4a514f09c/perilaku-masyarakat-di-masa-pandemi-covid-19.html). Therefore, strict health protocols (washing hands, wearing masks, physical distancing), implementation of maximum capacity, and operating hours rules in places with open-air conditions and crowds, particularly tourist attractions in areas with high wind speeds, including coastal areas, are among several considerations for policymakers.

The majority of the previous studies employed case and wind speed data on similar days, although the reporting record did not represent the infection date, as the symptoms typically manifested after days of transmission. The time required for tracking and testing was equally essential, causing difficulty in determining the actual infection date. Therefore, to minimize possible bias, daily case and wind speed data were converted to a weekly form, based on a mean incubation period (symptom appearance) of between five and six days, with a maximum of 14 days.²⁵

This study is expected to consider certain limitations that may have influenced the overall results. No analysis was conducted using time lags, and, therefore, it was not possible to determine the most significant relationship between the wind speed and the COVID-19 case variables between week one (t) and subsequent intervals ((t+1), (t+2), and so on). In addition, the unavailability of variable data in smaller regions, neighborhood/community associations (Rukun Tetangga/Rukun Warga), produced indefinite distribution patterns. However, other risk factors, including population size, density, mobility and immunity, community behavior (washing hands, wearing masks, and physical distancing), and the nature of the virus were not included in this research. In addition, the limited period of data collection (seven months) possibly influenced the analysis. Furthermore, a comparative analysis of the relationship between wind speed variables and case variables was performed per week, every 14 days, and monthly, indicating that the time frames were closely related. Therefore, further studies are expected to consider the abovementioned factors and generate significant improvements. Low wind speeds are associated with a high concentration of air pollutants, and therefore may promote a longer permanence of viral particles in polluted air of cities, thus favoring an indirect means of diffusion of the novel coronavirus (SARS-CoV-2).²⁶ Since this research did not include concentration of pollutants, therefore, it is suggested that future research should involve pollution levels in the area of the study to achieve comprehensive results.

Conclusions

Based on the overall results, environments with high average wind speeds tend to demonstrate an increased number of COVID-19 cases, particularly in coastal regions. This factor also accelerates airborne SARS-CoV-2 spread among people that do not correctly apply the prescribed health protocols. Therefore, the impure cases are triggered by the rate of the wind-speed-based transmission. Consequently, strict health protocols (washing hands, wearing masks, physical distancing), applying maximum capacity, and regulating work hours in places with open-air conditions and crowd potentials, especially tourist attractions in areas with high wind speeds, including coastal regions, serve as a basis for consideration in policymaking.

Ethical approval

This research was approved by the Research and Community Engagement Ethical Committee, Faculty of Public Health, Universitas Indonesia, No. 210/UN2.F10. D11/PPM.00.02/2021.

Data availability

Underlying data

Dryad. Wind Speed Data. https://doi:10.5061/dryad.41ns1rnj9 ²⁷

This project contains the following underlying data:

- Wind_speed_data1.csv
- README_wind_sd.md

Special Capital Region of Jakarta Provincial Health Office COVID-19. https://corona.jakarta.go.id/id/data-pemantauan ²⁸

- Data on Covid-19 cases

Meteorology, Climatology, and Geophysics Agency. https://dataonline.bmkg.go.id/akses_data ²⁹

- Wind speed data

GADM Map and Data. https://gadm.org/maps/IDN/jakartaraya.html ³⁰

- The base map for the Special Capital Region of Jakarta

Longitude and Latitude/GPS coordinates. https://www.gps-latitude-longitude.com/ ³¹

- Coordinates of the weather monitoring station

Acknowledgments

The authors are grateful to the Directorate Research and Development Universitas Indonesia through PUTI Grant for financial support.

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Version 3

VERSION 3 PUBLISHED 08 Feb 2023

Author details Author details

Dewi Susanna
Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Validation, Visualization, Writing – Review & Editing

Yoerdy Agusmal Saputra
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Visualization, Writing – Original Draft Preparation

Sandeep Poddar
Roles: Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This research was funded by the Directorate Research and Development Universitas Indonesia through Nota Dinas No: PENG-006/UN2.RST/PPM.00.00/2022 and Contract No. NKB-1283/UN2.RST/HKP.05.00/2022 International Publication Q2 Grant year 2022–2023
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (3)

version 3

Revised

Published: 28 Dec 2023, 12:145

https://doi.org/10.12688/f1000research.128908.3

version 2

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Published: 31 Aug 2023, 12:145

https://doi.org/10.12688/f1000research.128908.2

version 1

Published: 08 Feb 2023, 12:145

https://doi.org/10.12688/f1000research.128908.1

© 2023 Susanna D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Susanna D, Saputra YA and Poddar S. Analysis of the effect of wind speed in increasing the COVID-19 cases in Jakarta [version 1; peer review: 1 approved with reservations]. F1000Research 2023, 12:145 (https://doi.org/10.12688/f1000research.128908.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

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PUBLISHED 08 Feb 2023

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Reviewer Report 24 Mar 2023

Edmund I. Yamba, Department of Meteorology and Climate Science, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana

Approved with Reservations

https://doi.org/10.5256/f1000research.141547.r164423

In this study, the authors examined the effect of wind speed on COVID-19 outcome in Jakarta, Indonesia. Using maximum and mean wind speed data from the Indonesian Meteorology, Climatology and Geophysics Agency; and weekly COVID-19 cases from the special coronavirus website of Jakarta Provincial Health Office, they established the link between wind speed and COVID-19 cases via correlation, graphic/time trend, and spatial analysis. Their results showed that high wind speeds were associated with an increased number of COVID-19 cases, particularly in coastal regions. Hence, the authors concluded that wind speed plays a role in the transmission of COVID-19, especially the coastal areas of Jakarta.

This study is significant and offers valuable insights into the relationship between wind speed and the transmission of the COVID-19 virus in Jakarta. The findings have practical implications for the implementation of health protocols in areas with high wind speed to mitigate the spread of the virus. The presentation of the study is clear, engaging and comprehensible. Nonetheless, the manuscript requires revision to enhance its quality. Below are my comments regarding areas that need improvement.

1. The title vs the content presented: Based on my interpretation, it appears that the authors seek to establish the association between wind speed and COVID-19 cases in Jakarta, without delving into causality, as evidenced by their utilization of Pearson's correlation analysis. The use of the term "effect" in the title implies a notion of causality. Nevertheless, the research work put forth does not explicitly establish causality, but rather investigates the existence of a relationship through inferences. It is recommended that the authors reconsider the phrasing of the title to reflect the content of their study. For example, something like “The relationship between wind speed and COVID-19 infections in Jakarta”.

2. Introduction/novelty: Despite the work's overall satisfactory and engaging presentation, the authors have not explicitly addressed the existing research gaps and their contributions to filling them. Previous studies have established the relationship between wind speed and the spread of COVID-19, which raises questions about the motivation behind the authors' replication of this work in Jakarta. Specifically, what were the compelling research questions that drove this inquiry and how were they answered? Hence, the originality or novelty of the work appears to be limited, and the authors should strive to enhance it. One suggestion for improvement could be to extend beyond the typical approach of establishing correlations between weather variables and COVID-19, which is prevalent in most existing literature. Merely identifying a correlation between two variables is insufficient to determine the causative relationship between them. Therefore, panel estimation techniques, for example, could be explored by the authors to investigate the causal aspect further.

3. Data collection: The authors conducted a study that involved the collection of daily wind speed and COVID-19 case data spanning March to September 2020, which was then converted into weekly data for 31 weeks. The rationale for the conversion remains unclear, and it would be beneficial if the authors could elucidate their reasons for doing so and how this approach facilitated more effective analysis. Additionally, it is essential to understand how gaps in the data were managed as the quality of the data used is fundamental to the reliability of the results obtained. The authors reported that the study was conducted in Jakarta province, consisting of 261 urban villages. However, it is not evident whether the data used were obtained from each of these villages and aggregated for the province. If this was the case, it would be useful to know whether data were available for each village. It is recommended that the authors provide more comprehensive details regarding the availability and quality of the data used in their analysis to enhance the reliability of their findings.

4. Statistical data analysis: The authors employed Pearson's product-moment correlation test to assess the association between wind speed (independent variable) and COVID-19 cases (dependent variable). However, it is important to note that Pearson's correlation test necessitates the use of normally distributed data. As the daily COVID-19 and wind speed data were not normally distributed, the utilization of Pearson's approach may prove problematic. Therefore, it is imperative that the authors examine the normality of the daily or weekly data used to inform their selection of the appropriate correlation test. In this context, Spearman's correlation test may be suitable since it does not rely on the approximate normality of the data. Furthermore, the authors categorized the strengths of the correlation between wind speed and COVID-19 into four groups: absence/weak relationship (r = 0.00–0.25), moderate (r = 0.26–0.50), strong (r = 0.51–0.75), and very strong/perfect (r = 0.76–1.00). It is necessary to elucidate the scientific rationale for this division and the guidelines employed. This will enhance the clarity and reliability of the authors' findings and recommendations.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Main research area is Biometeorology and Bioclimatology with current focus on weather and climate driven infectious diseases, extreme heat and human health, air pollution and respiratory infections, weather and climate extreme impacts, tropical climate variability, climate modelling and Urban Heat Island (UHI) warming,

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

Report a concern

Author Response 29 Nov 2023

Dewi Susanna, Department of Environmental Health, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia

29 Nov 2023

Author Response
Dear Dr. Edmund I. Yamba
Department of Meteorology and Climate Science, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Thank you very much for the valuable comments.
Here, ... Continue reading
Dear Dr. Edmund I. Yamba
Department of Meteorology and Climate Science, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Thank you very much for the valuable comments.
Here, we tried to respond your comments as follow:

The title vs the content presented:

Based on your interpretation, it appears that the authors seek to establish the association between wind speed and COVID-19 cases in Jakarta, without delving into causality, as evidenced by their utilization of Pearson's correlation analysis. The use of the term "effect" in the title implies a notion of causality. Nevertheless, the research work put forth does not explicitly establish causality, but rather investigates the existence of a relationship through inferences. It is recommended that the authors reconsider the phrasing of the title to reflect the content of their study. For example, something like “The relationship between wind speed and COVID-19 infections in Jakarta”.

Response:
Thank you for the proper title. The title of the article has been revised to "The relationship between wind speed and COVID-19 infections in Jakarta"

Introduction/novelty:

Despite the work's overall satisfactory and engaging presentation, the authors have not explicitly addressed the existing research gaps and their contributions to filling them. Previous studies have established the relationship between wind speed and the spread of COVID-19, which raises questions about the motivation behind the authors' replication of this work in Jakarta. Specifically, what were the compelling research questions that drove this inquiry and how were they answered? Hence, the originality or novelty of the work appears to be limited, and the authors should strive to enhance it. One suggestion for improvement could be to extend beyond the typical approach of establishing correlations between weather variables and COVID-19, which is prevalent in most existing literature. Merely identifying a correlation between two variables is insufficient to determine the causative relationship between them. Therefore, panel estimation techniques, for example, could be explored by the authors to investigate the causal aspect further.

Responses:
The previous research gaps and the researcher's contribution in filling the previous research gaps (novelty) have been added in the introduction section:
“In several countries, researchers have found that high wind speeds play a role in the increase of COVID-19 cases.^7–12 This is because SARS-CoV-2 can survive in the air, so wind speeds accelerate the spread of the virus by carrying droplets to various destinations.⁷
In the internal environment, higher wind speeds decrease the viral load indoors, which can prevent SARS outbreaks.¹⁴ Other researchers have found that high wind speeds can actually suppress the spread of COVID-19 cases.^15–17
This study considers some of the limitations of previous studies by examining the potential of wind speed in influencing the spread of cases based on regional characteristics^{7,9–12,15,17} because environmental conditions between regions are different.¹⁸ In addition, daily COVID-19 case data were converted into weekly cases to adjust for the incubation period of COVID-19 (5-6 days) and the uncertain time interval between the first day of infection and the day of sampling.¹⁹
As well as the potential spread of cases based on regional characteristics in DKI Jakarta, especially in coastal areas”.

Data collection:

The authors conducted a study that involved the collection of daily wind speed and COVID-19 case data spanning March to September 2020, which was then converted into weekly data for 31 weeks. The rationale for the conversion remains unclear, and it would be beneficial if the authors could elucidate their reasons for doing so and how this approach facilitated more effective analysis. Additionally, it is essential to understand how gaps in the data were managed as the quality of the data used is fundamental to the reliability of the results obtained. The authors reported that the study was conducted in Jakarta province, consisting of 261 urban villages. However, it is not evident whether the data used were obtained from each of these villages and aggregated for the province. If this was the case, it would be useful to know whether data were available for each village. It is recommended that the authors provide more comprehensive details regarding the availability and quality of the data used in their analysis to enhance the reliability of their findings.

Responses:

The reason for converting daily covid-19 case data into weekly covid-19 case data has been added to the data collection section:

The conversion of daily cases to weekly cases is related to several considerations, such as the incubation period of COVID-19, which lasts for 5–6 days on average, and the uncertain time interval between the first day of infection and the day of sampling.¹⁹

An explanation of how to obtain covid-19 case data in 261 villages has been adjusted in the data collection section:

These data were obtained from case reports of hospitals, health centers, and laboratories recorded per urban village by the Jakarta Provincial Health Office and can be accessed on the Jakarta Provincial Health Office website (https://corona.jakarta.go.id/id/data-pemantauan) and reports of two weather monitoring stations (Kemayoran station and Tanjung Priok station) in Jakarta, which can be accessed on the website of the Meteorology, Climatology, and Geophysics Agency (https://dataonline.bmkg.go.id/akses_data).

Statistical data analysis:

The authors employed Pearson's product-moment correlation test to assess the association between wind speed (independent variable) and COVID-19 cases (dependent variable). However, it is important to note that Pearson's correlation test necessitates the use of normally distributed data. As the daily COVID-19 and wind speed data were not normally distributed, the utilization of Pearson's approach may prove problematic. Therefore, it is imperative that the authors examine the normality of the daily or weekly data used to inform their selection of the appropriate correlation test. In this context, Spearman's correlation test may be suitable since it does not rely on the approximate normality of the data. Furthermore, the authors categorized the strengths of the correlation between wind speed and COVID-19 into four groups: absence/weak relationship (r = 0.00–0.25), moderate (r = 0.26–0.50), strong (r = 0.51–0.75), and very strong/perfect (r = 0.76–1.00). It is necessary to elucidate the scientific rationale for this division and the guidelines employed. This will enhance the clarity and reliability of the authors' findings and recommendations.

Responses:

An explanation of the use of the pearson-product moment test has been added to the statistical data analysis section:

Before the correlation test is applied, the data normality test is conducted first. The results of the normality analysis show that the maximum wind speed variable is normally distributed, while the average wind speed and COVID-19 cases are not normally distributed. From these results, variables that are not normally distributed are then transformed by the square root (x) to obtain normality. This step is taken to fulfil the requirements for multiple linear regression analysis (normality assumption).

The source of the correlation strength categorization has been provided and the reason for categorizing the correlation strength has been added:

In addition, the strengths of the association were qualitatively divided into four categories, where r = 0.00–0.25 was absence/weak relationship, r = 0.26–0.50 was moderate, r = 0.51–0.75 was strong and r = 0.76–1.00 was very strong/perfect.²⁰
This categorization aims to determine how strongly the independent variables (maximum wind speed and average wind speed) correlate with the dependent variable (COVID-19 cases). It is also used to discuss the results of the analysis in the discussion section.

Thank you for all the input so this article became more comprehensives and understandable for the readers.
Dear Dr. Edmund I. Yamba
Department of Meteorology and Climate Science, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Thank you very much for the valuable comments.
Here, we tried to respond your comments as follow:

The title vs the content presented:

Based on your interpretation, it appears that the authors seek to establish the association between wind speed and COVID-19 cases in Jakarta, without delving into causality, as evidenced by their utilization of Pearson's correlation analysis. The use of the term "effect" in the title implies a notion of causality. Nevertheless, the research work put forth does not explicitly establish causality, but rather investigates the existence of a relationship through inferences. It is recommended that the authors reconsider the phrasing of the title to reflect the content of their study. For example, something like “The relationship between wind speed and COVID-19 infections in Jakarta”.

Response:
Thank you for the proper title. The title of the article has been revised to "The relationship between wind speed and COVID-19 infections in Jakarta"

Introduction/novelty:

Despite the work's overall satisfactory and engaging presentation, the authors have not explicitly addressed the existing research gaps and their contributions to filling them. Previous studies have established the relationship between wind speed and the spread of COVID-19, which raises questions about the motivation behind the authors' replication of this work in Jakarta. Specifically, what were the compelling research questions that drove this inquiry and how were they answered? Hence, the originality or novelty of the work appears to be limited, and the authors should strive to enhance it. One suggestion for improvement could be to extend beyond the typical approach of establishing correlations between weather variables and COVID-19, which is prevalent in most existing literature. Merely identifying a correlation between two variables is insufficient to determine the causative relationship between them. Therefore, panel estimation techniques, for example, could be explored by the authors to investigate the causal aspect further.

Responses:
The previous research gaps and the researcher's contribution in filling the previous research gaps (novelty) have been added in the introduction section:
“In several countries, researchers have found that high wind speeds play a role in the increase of COVID-19 cases.^7–12 This is because SARS-CoV-2 can survive in the air, so wind speeds accelerate the spread of the virus by carrying droplets to various destinations.⁷
In the internal environment, higher wind speeds decrease the viral load indoors, which can prevent SARS outbreaks.¹⁴ Other researchers have found that high wind speeds can actually suppress the spread of COVID-19 cases.^15–17
This study considers some of the limitations of previous studies by examining the potential of wind speed in influencing the spread of cases based on regional characteristics^{7,9–12,15,17} because environmental conditions between regions are different.¹⁸ In addition, daily COVID-19 case data were converted into weekly cases to adjust for the incubation period of COVID-19 (5-6 days) and the uncertain time interval between the first day of infection and the day of sampling.¹⁹
As well as the potential spread of cases based on regional characteristics in DKI Jakarta, especially in coastal areas”.

Data collection:

The authors conducted a study that involved the collection of daily wind speed and COVID-19 case data spanning March to September 2020, which was then converted into weekly data for 31 weeks. The rationale for the conversion remains unclear, and it would be beneficial if the authors could elucidate their reasons for doing so and how this approach facilitated more effective analysis. Additionally, it is essential to understand how gaps in the data were managed as the quality of the data used is fundamental to the reliability of the results obtained. The authors reported that the study was conducted in Jakarta province, consisting of 261 urban villages. However, it is not evident whether the data used were obtained from each of these villages and aggregated for the province. If this was the case, it would be useful to know whether data were available for each village. It is recommended that the authors provide more comprehensive details regarding the availability and quality of the data used in their analysis to enhance the reliability of their findings.

Responses:

The reason for converting daily covid-19 case data into weekly covid-19 case data has been added to the data collection section:

The conversion of daily cases to weekly cases is related to several considerations, such as the incubation period of COVID-19, which lasts for 5–6 days on average, and the uncertain time interval between the first day of infection and the day of sampling.¹⁹

An explanation of how to obtain covid-19 case data in 261 villages has been adjusted in the data collection section:

These data were obtained from case reports of hospitals, health centers, and laboratories recorded per urban village by the Jakarta Provincial Health Office and can be accessed on the Jakarta Provincial Health Office website (https://corona.jakarta.go.id/id/data-pemantauan) and reports of two weather monitoring stations (Kemayoran station and Tanjung Priok station) in Jakarta, which can be accessed on the website of the Meteorology, Climatology, and Geophysics Agency (https://dataonline.bmkg.go.id/akses_data).

Statistical data analysis:

The authors employed Pearson's product-moment correlation test to assess the association between wind speed (independent variable) and COVID-19 cases (dependent variable). However, it is important to note that Pearson's correlation test necessitates the use of normally distributed data. As the daily COVID-19 and wind speed data were not normally distributed, the utilization of Pearson's approach may prove problematic. Therefore, it is imperative that the authors examine the normality of the daily or weekly data used to inform their selection of the appropriate correlation test. In this context, Spearman's correlation test may be suitable since it does not rely on the approximate normality of the data. Furthermore, the authors categorized the strengths of the correlation between wind speed and COVID-19 into four groups: absence/weak relationship (r = 0.00–0.25), moderate (r = 0.26–0.50), strong (r = 0.51–0.75), and very strong/perfect (r = 0.76–1.00). It is necessary to elucidate the scientific rationale for this division and the guidelines employed. This will enhance the clarity and reliability of the authors' findings and recommendations.

Responses:

An explanation of the use of the pearson-product moment test has been added to the statistical data analysis section:

Before the correlation test is applied, the data normality test is conducted first. The results of the normality analysis show that the maximum wind speed variable is normally distributed, while the average wind speed and COVID-19 cases are not normally distributed. From these results, variables that are not normally distributed are then transformed by the square root (x) to obtain normality. This step is taken to fulfil the requirements for multiple linear regression analysis (normality assumption).

The source of the correlation strength categorization has been provided and the reason for categorizing the correlation strength has been added:

In addition, the strengths of the association were qualitatively divided into four categories, where r = 0.00–0.25 was absence/weak relationship, r = 0.26–0.50 was moderate, r = 0.51–0.75 was strong and r = 0.76–1.00 was very strong/perfect.²⁰
This categorization aims to determine how strongly the independent variables (maximum wind speed and average wind speed) correlate with the dependent variable (COVID-19 cases). It is also used to discuss the results of the analysis in the discussion section.

Thank you for all the input so this article became more comprehensives and understandable for the readers.
Competing Interests: We do not have any conflict of interests. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 29 Nov 2023

Dewi Susanna, Department of Environmental Health, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia

29 Nov 2023

Author Response
Dear Dr. Edmund I. Yamba
Department of Meteorology and Climate Science, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Thank you very much for the valuable comments.
Here, ... Continue reading
Dear Dr. Edmund I. Yamba
Department of Meteorology and Climate Science, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Thank you very much for the valuable comments.
Here, we tried to respond your comments as follow:

The title vs the content presented:

Based on your interpretation, it appears that the authors seek to establish the association between wind speed and COVID-19 cases in Jakarta, without delving into causality, as evidenced by their utilization of Pearson's correlation analysis. The use of the term "effect" in the title implies a notion of causality. Nevertheless, the research work put forth does not explicitly establish causality, but rather investigates the existence of a relationship through inferences. It is recommended that the authors reconsider the phrasing of the title to reflect the content of their study. For example, something like “The relationship between wind speed and COVID-19 infections in Jakarta”.

Response:
Thank you for the proper title. The title of the article has been revised to "The relationship between wind speed and COVID-19 infections in Jakarta"

Introduction/novelty:

Despite the work's overall satisfactory and engaging presentation, the authors have not explicitly addressed the existing research gaps and their contributions to filling them. Previous studies have established the relationship between wind speed and the spread of COVID-19, which raises questions about the motivation behind the authors' replication of this work in Jakarta. Specifically, what were the compelling research questions that drove this inquiry and how were they answered? Hence, the originality or novelty of the work appears to be limited, and the authors should strive to enhance it. One suggestion for improvement could be to extend beyond the typical approach of establishing correlations between weather variables and COVID-19, which is prevalent in most existing literature. Merely identifying a correlation between two variables is insufficient to determine the causative relationship between them. Therefore, panel estimation techniques, for example, could be explored by the authors to investigate the causal aspect further.

Responses:
The previous research gaps and the researcher's contribution in filling the previous research gaps (novelty) have been added in the introduction section:
“In several countries, researchers have found that high wind speeds play a role in the increase of COVID-19 cases.^7–12 This is because SARS-CoV-2 can survive in the air, so wind speeds accelerate the spread of the virus by carrying droplets to various destinations.⁷
In the internal environment, higher wind speeds decrease the viral load indoors, which can prevent SARS outbreaks.¹⁴ Other researchers have found that high wind speeds can actually suppress the spread of COVID-19 cases.^15–17
This study considers some of the limitations of previous studies by examining the potential of wind speed in influencing the spread of cases based on regional characteristics^{7,9–12,15,17} because environmental conditions between regions are different.¹⁸ In addition, daily COVID-19 case data were converted into weekly cases to adjust for the incubation period of COVID-19 (5-6 days) and the uncertain time interval between the first day of infection and the day of sampling.¹⁹
As well as the potential spread of cases based on regional characteristics in DKI Jakarta, especially in coastal areas”.

Data collection:

The authors conducted a study that involved the collection of daily wind speed and COVID-19 case data spanning March to September 2020, which was then converted into weekly data for 31 weeks. The rationale for the conversion remains unclear, and it would be beneficial if the authors could elucidate their reasons for doing so and how this approach facilitated more effective analysis. Additionally, it is essential to understand how gaps in the data were managed as the quality of the data used is fundamental to the reliability of the results obtained. The authors reported that the study was conducted in Jakarta province, consisting of 261 urban villages. However, it is not evident whether the data used were obtained from each of these villages and aggregated for the province. If this was the case, it would be useful to know whether data were available for each village. It is recommended that the authors provide more comprehensive details regarding the availability and quality of the data used in their analysis to enhance the reliability of their findings.

Responses:

The reason for converting daily covid-19 case data into weekly covid-19 case data has been added to the data collection section:

The conversion of daily cases to weekly cases is related to several considerations, such as the incubation period of COVID-19, which lasts for 5–6 days on average, and the uncertain time interval between the first day of infection and the day of sampling.¹⁹

An explanation of how to obtain covid-19 case data in 261 villages has been adjusted in the data collection section:

These data were obtained from case reports of hospitals, health centers, and laboratories recorded per urban village by the Jakarta Provincial Health Office and can be accessed on the Jakarta Provincial Health Office website (https://corona.jakarta.go.id/id/data-pemantauan) and reports of two weather monitoring stations (Kemayoran station and Tanjung Priok station) in Jakarta, which can be accessed on the website of the Meteorology, Climatology, and Geophysics Agency (https://dataonline.bmkg.go.id/akses_data).

Statistical data analysis:

The authors employed Pearson's product-moment correlation test to assess the association between wind speed (independent variable) and COVID-19 cases (dependent variable). However, it is important to note that Pearson's correlation test necessitates the use of normally distributed data. As the daily COVID-19 and wind speed data were not normally distributed, the utilization of Pearson's approach may prove problematic. Therefore, it is imperative that the authors examine the normality of the daily or weekly data used to inform their selection of the appropriate correlation test. In this context, Spearman's correlation test may be suitable since it does not rely on the approximate normality of the data. Furthermore, the authors categorized the strengths of the correlation between wind speed and COVID-19 into four groups: absence/weak relationship (r = 0.00–0.25), moderate (r = 0.26–0.50), strong (r = 0.51–0.75), and very strong/perfect (r = 0.76–1.00). It is necessary to elucidate the scientific rationale for this division and the guidelines employed. This will enhance the clarity and reliability of the authors' findings and recommendations.

Responses:

An explanation of the use of the pearson-product moment test has been added to the statistical data analysis section:

Before the correlation test is applied, the data normality test is conducted first. The results of the normality analysis show that the maximum wind speed variable is normally distributed, while the average wind speed and COVID-19 cases are not normally distributed. From these results, variables that are not normally distributed are then transformed by the square root (x) to obtain normality. This step is taken to fulfil the requirements for multiple linear regression analysis (normality assumption).

The source of the correlation strength categorization has been provided and the reason for categorizing the correlation strength has been added:

In addition, the strengths of the association were qualitatively divided into four categories, where r = 0.00–0.25 was absence/weak relationship, r = 0.26–0.50 was moderate, r = 0.51–0.75 was strong and r = 0.76–1.00 was very strong/perfect.²⁰
This categorization aims to determine how strongly the independent variables (maximum wind speed and average wind speed) correlate with the dependent variable (COVID-19 cases). It is also used to discuss the results of the analysis in the discussion section.

Thank you for all the input so this article became more comprehensives and understandable for the readers.
Dear Dr. Edmund I. Yamba
Department of Meteorology and Climate Science, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Thank you very much for the valuable comments.
Here, we tried to respond your comments as follow:

The title vs the content presented:

Based on your interpretation, it appears that the authors seek to establish the association between wind speed and COVID-19 cases in Jakarta, without delving into causality, as evidenced by their utilization of Pearson's correlation analysis. The use of the term "effect" in the title implies a notion of causality. Nevertheless, the research work put forth does not explicitly establish causality, but rather investigates the existence of a relationship through inferences. It is recommended that the authors reconsider the phrasing of the title to reflect the content of their study. For example, something like “The relationship between wind speed and COVID-19 infections in Jakarta”.

Response:
Thank you for the proper title. The title of the article has been revised to "The relationship between wind speed and COVID-19 infections in Jakarta"

Introduction/novelty:

Despite the work's overall satisfactory and engaging presentation, the authors have not explicitly addressed the existing research gaps and their contributions to filling them. Previous studies have established the relationship between wind speed and the spread of COVID-19, which raises questions about the motivation behind the authors' replication of this work in Jakarta. Specifically, what were the compelling research questions that drove this inquiry and how were they answered? Hence, the originality or novelty of the work appears to be limited, and the authors should strive to enhance it. One suggestion for improvement could be to extend beyond the typical approach of establishing correlations between weather variables and COVID-19, which is prevalent in most existing literature. Merely identifying a correlation between two variables is insufficient to determine the causative relationship between them. Therefore, panel estimation techniques, for example, could be explored by the authors to investigate the causal aspect further.

Responses:
The previous research gaps and the researcher's contribution in filling the previous research gaps (novelty) have been added in the introduction section:
“In several countries, researchers have found that high wind speeds play a role in the increase of COVID-19 cases.^7–12 This is because SARS-CoV-2 can survive in the air, so wind speeds accelerate the spread of the virus by carrying droplets to various destinations.⁷
In the internal environment, higher wind speeds decrease the viral load indoors, which can prevent SARS outbreaks.¹⁴ Other researchers have found that high wind speeds can actually suppress the spread of COVID-19 cases.^15–17
This study considers some of the limitations of previous studies by examining the potential of wind speed in influencing the spread of cases based on regional characteristics^{7,9–12,15,17} because environmental conditions between regions are different.¹⁸ In addition, daily COVID-19 case data were converted into weekly cases to adjust for the incubation period of COVID-19 (5-6 days) and the uncertain time interval between the first day of infection and the day of sampling.¹⁹
As well as the potential spread of cases based on regional characteristics in DKI Jakarta, especially in coastal areas”.

Data collection:

The authors conducted a study that involved the collection of daily wind speed and COVID-19 case data spanning March to September 2020, which was then converted into weekly data for 31 weeks. The rationale for the conversion remains unclear, and it would be beneficial if the authors could elucidate their reasons for doing so and how this approach facilitated more effective analysis. Additionally, it is essential to understand how gaps in the data were managed as the quality of the data used is fundamental to the reliability of the results obtained. The authors reported that the study was conducted in Jakarta province, consisting of 261 urban villages. However, it is not evident whether the data used were obtained from each of these villages and aggregated for the province. If this was the case, it would be useful to know whether data were available for each village. It is recommended that the authors provide more comprehensive details regarding the availability and quality of the data used in their analysis to enhance the reliability of their findings.

Responses:

The reason for converting daily covid-19 case data into weekly covid-19 case data has been added to the data collection section:

The conversion of daily cases to weekly cases is related to several considerations, such as the incubation period of COVID-19, which lasts for 5–6 days on average, and the uncertain time interval between the first day of infection and the day of sampling.¹⁹

An explanation of how to obtain covid-19 case data in 261 villages has been adjusted in the data collection section:

These data were obtained from case reports of hospitals, health centers, and laboratories recorded per urban village by the Jakarta Provincial Health Office and can be accessed on the Jakarta Provincial Health Office website (https://corona.jakarta.go.id/id/data-pemantauan) and reports of two weather monitoring stations (Kemayoran station and Tanjung Priok station) in Jakarta, which can be accessed on the website of the Meteorology, Climatology, and Geophysics Agency (https://dataonline.bmkg.go.id/akses_data).

Statistical data analysis:

The authors employed Pearson's product-moment correlation test to assess the association between wind speed (independent variable) and COVID-19 cases (dependent variable). However, it is important to note that Pearson's correlation test necessitates the use of normally distributed data. As the daily COVID-19 and wind speed data were not normally distributed, the utilization of Pearson's approach may prove problematic. Therefore, it is imperative that the authors examine the normality of the daily or weekly data used to inform their selection of the appropriate correlation test. In this context, Spearman's correlation test may be suitable since it does not rely on the approximate normality of the data. Furthermore, the authors categorized the strengths of the correlation between wind speed and COVID-19 into four groups: absence/weak relationship (r = 0.00–0.25), moderate (r = 0.26–0.50), strong (r = 0.51–0.75), and very strong/perfect (r = 0.76–1.00). It is necessary to elucidate the scientific rationale for this division and the guidelines employed. This will enhance the clarity and reliability of the authors' findings and recommendations.

Responses:

An explanation of the use of the pearson-product moment test has been added to the statistical data analysis section:

Before the correlation test is applied, the data normality test is conducted first. The results of the normality analysis show that the maximum wind speed variable is normally distributed, while the average wind speed and COVID-19 cases are not normally distributed. From these results, variables that are not normally distributed are then transformed by the square root (x) to obtain normality. This step is taken to fulfil the requirements for multiple linear regression analysis (normality assumption).

The source of the correlation strength categorization has been provided and the reason for categorizing the correlation strength has been added:

In addition, the strengths of the association were qualitatively divided into four categories, where r = 0.00–0.25 was absence/weak relationship, r = 0.26–0.50 was moderate, r = 0.51–0.75 was strong and r = 0.76–1.00 was very strong/perfect.²⁰
This categorization aims to determine how strongly the independent variables (maximum wind speed and average wind speed) correlate with the dependent variable (COVID-19 cases). It is also used to discuss the results of the analysis in the discussion section.

Thank you for all the input so this article became more comprehensives and understandable for the readers.
Competing Interests: We do not have any conflict of interests. Close
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Version 3

VERSION 3 PUBLISHED 08 Feb 2023

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Version 3 (revision) 28 Dec 23		read
Version 2 (revision) 31 Aug 23		read
Version 1 08 Feb 23	read

Edmund I. Yamba, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Mario Coccia, Collegio Carlo Alberto, Moncalieri, Italy; Arizona State University (Ringgold ID: 7864), Tempe, USA

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Reviewer Report

11 Views

23 Jan 2024 | for Version 3

Mario Coccia, CNR -- National Research Council of Italy, Collegio Carlo Alberto, Moncalieri, Italy; Arizona State University (Ringgold ID: 7864), Tempe, arizona, USA

11 Views Cite this report Responses(0)

Approved

I have read thoroughly the revised version of paper.
The authors have done considerable additional work, and addressed all concerns and criticisms in the revised manuscript, which I believe has improved substantially in the theoretical framework, study design and discussion of results.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

COVID-19 and Environment

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report

10 Views

02 Nov 2023 | for Version 2

Mario Coccia, CNR -- National Research Council of Italy, Collegio Carlo Alberto, Moncalieri, Italy; Arizona State University (Ringgold ID: 7864), Tempe, arizona, USA

10 Views Cite this report Responses(1)

Approved With Reservations

Analysis of the effect of wind speed in increasing the COVID-19 cases in Jakarta

The topics of this paper are interesting, though well known. The structure and content must be revised, and results have to be better explained by authors before to be reconsidered for indexing.

Title has to clarify the period under study.

Abstract has to clarify the goal and environmental, health and social implications.

Introduction has to better clarify the research questions of this study and provide more theoretical background. Authors have to better describe the different sources of transmission dynamics of COVID-19 (e.g., climate, air pollution, density, etc.) and risk factors in society, which can accelerate diffusion of this novel coronavirus in environment. The study of wind speed has to be associated with temperature and air pollution to well explain transmission of COVID-19 and similar diseases. After that they can focus on the topics of this study to provide a correct analysis for fruitful discussion (See suggested readings that must be all read and used in the text).

Methods of this study is not clear. The section of Materials and methods must be re-structured with following three sections only and same order:

Sample and data
Measures of variables
Models and Data analysis procedure.

Results

Authors have to avoid a lot of subheadings that create fragmentation and confusion. If necessary, can use bullet points (same comments for section of results and all sections).
Figure 1 has to indicate the year in title to be clear.
The two graphs can be merged in a single figure with two lines to be clear. Figures 2 and 3 are so small that cannot be read. Provide a note to the figure with more description. Or put them in appendix with larger size per each month.

Discussion.

First, authors have to synthesize the main results in a simple table to be clear for readers and then show what this study adds compared to other studies.

Conclusion so short it is useless. Conclusion has not to be a summary, but authors have to focus on manifold limitations of this study and provide suggestions of environmental, health, crisis management and social policy, as well as how nations can prevent with good environmental and health policies.

Overall, then, the paper is interesting, but structure is confused. Theoretical framework is weak, and some results create confusion… structure of the paper has to be improved; study design, discussion and presentation of results have to be clarified using suggested comments.

I strongly suggest improving the paper, by using all comments (suggested papers included to read and use all¹^-¹⁸) and maybe it can be considered.

In particular, an accurate reading and use of following studies can reinforce the theoretical framework of this study, support the discussion of research in a comparative analysis of other context studied, so authors can provide fruitful implications for policymaker to design an health policy to face pandemic crisis in context as Jakarta and cities having similar climate, environmental and atmospheric factors.

Suggested readings of relevant papers that have to be read and all inserted in the text and references¹^-¹⁸.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Srivastava A: COVID-19 and air pollution and meteorology-an intricate relationship: A review.Chemosphere. 2021; 263: 128297 PubMed Abstract | Publisher Full Text
2. Coccia M: Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency.AIMS Public Health. 2023; 10 (1): 145-168 PubMed Abstract | Publisher Full Text
3. Xie J, Zhu Y: Association between ambient temperature and COVID-19 infection in 122 cities from China.Sci Total Environ. 2020; 724: 138201 PubMed Abstract | Publisher Full Text
4. Coccia M: Preparedness of countries to face COVID-19 pandemic crisis: Strategic positioning and factors supporting effective strategies of prevention of pandemic threats.Environ Res. 2022; 203: 111678 PubMed Abstract | Publisher Full Text
5. Bashir MF, Ma BJ, Bilal, Komal B, et al.: Correlation between environmental pollution indicators and COVID-19 pandemic: A brief study in Californian context.Environ Res. 2020; 187: 109652 PubMed Abstract | Publisher Full Text
6. Núñez-Delgado A, Bontempi E, Coccia M, Kumar M, et al.: SARS-CoV-2 and other pathogenic microorganisms in the environment. Environmental Research. 2021; 201. Publisher Full Text
7. Xu K, Cui K, Young L, Hsieh Y, et al.: Impact of the COVID-19 Event on Air Quality in Central China. Aerosol and Air Quality Research. 2020; 20 (5): 915-929 Publisher Full Text
8. Benati I, Coccia M: Global analysis of timely COVID-19 vaccinations: improving governance to reinforce response policies for pandemic crises. International Journal of Health Governance. 2022; 27 (3): 240-253 Publisher Full Text
9. Coccia M: The impact of first and second wave of the COVID-19 pandemic in society: comparative analysis to support control measures to cope with negative effects of future infectious diseases.Environ Res. 2021; 197: 111099 PubMed Abstract | Publisher Full Text
10. Rosario DKA, Mutz YS, Bernardes PC, Conte-Junior CA: Relationship between COVID-19 and weather: Case study in a tropical country.Int J Hyg Environ Health. 2020; 229: 113587 PubMed Abstract | Publisher Full Text
11. Coccia M: Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID.Sci Total Environ. 2020; 729: 138474 PubMed Abstract | Publisher Full Text
12. Shen L, Zhao T, Wang H, Liu J, et al.: Importance of meteorology in air pollution events during the city lockdown for COVID-19 in Hubei Province, Central China.Sci Total Environ. 2021; 754: 142227 PubMed Abstract | Publisher Full Text
13. Bontempi E, Coccia M, Vergalli S, Zanoletti A: Can commercial trade represent the main indicator of the COVID-19 diffusion due to human-to-human interactions? A comparative analysis between Italy, France, and Spain.Environ Res. 2021; 201: 111529 PubMed Abstract | Publisher Full Text
14. Haque SE, Rahman M: Association between temperature, humidity, and COVID-19 outbreaks in Bangladesh.Environ Sci Policy. 2020; 114: 253-255 PubMed Abstract | Publisher Full Text
15. Coccia M: The effects of atmospheric stability with low wind speed and of air pollution on the accelerated transmission dynamics of COVID-19. International Journal of Environmental Studies. 2021; 78 (1): 1-27 Publisher Full Text
16. Sarkodie SA, Owusu PA: Impact of meteorological factors on COVID-19 pandemic: Evidence from top 20 countries with confirmed cases.Environ Res. 2020; 191: 110101 PubMed Abstract | Publisher Full Text
17. Rahimi NR, Fouladi-Fard R, Aali R, Shahryari A, et al.: Bidirectional association between COVID-19 and the environment: A systematic review.Environ Res. 2021; 194: 110692 PubMed Abstract | Publisher Full Text
18. Coccia M: Pandemic Prevention: Lessons from COVID-19. Encyclopedia. 2021; 1 (2): 433-444 Publisher Full Text

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

COVID-19 and Environment

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Author Response

17 Jun 2024

Dewi Susanna, Department of Environmental Health, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia

Dear Mario Coccia,
CNR - National Research Council of Italy, Collegio Carlo Alberto, Moncalieri, Italy

Thank you very much for your deep review of our manuscript.
Here are our responses in accordance with your comments as mentioned below:

The title has to clarify the period under study.

Responses:
The title of the article has been revised to " The effect of wind speed in increasing COVID-19 cases in Jakarta: a spatial-temporal analysis from March to September 2020"

Abstract has to clarify the goal and environmental, health and social implications.

Responses:
The explanation of objectives in the abstract has been adjusted. Implications for the environment, health and social have been adjusted in the conclusion section.

Introduction has to be better clarify the research questions of this study and provide more theoretical background. Authors have to better describe the different sources of transmission dynamics of COVID-19 (e.g., climate, air pollution, density, etc.) and risk factors in society, which can accelerate diffusion of this novel coronavirus in environment. The study of wind speed has to be associated with temperature and air pollution to well explain transmission of COVID-19 and similar diseases. After that they can focus on the topics of this study to provide a correct analysis for fruitful discussion (See suggested readings that must be all read and used in the text).

Responses:
The introduction has been adjusted with the addition of other sources of COVID-19 transmission dynamics, according to the sources recommended by the reviewer:
Overall, the transmission dynamics of viral infections, such as COVID-19, are caused by systemic factors (biological characteristics of the virus, incubation period, etc.), specific factors (complex interactions between air pollution, meteorological conditions, etc.), biological characteristics of viral infectivity, and individual health levels (habits, immune system, age, gender, etc.) (Coccia, 2020). The role of climatic elements such as temperature, humidity, precipitation, wind speed, and sunlight are crucial in disease transmission because they are parameters that can determine the survival of the SARS virus in the environment (Rahimi et al., 2021; Rosario et al., 2020; Saputra et al., 2021; Saputra & Susanna, 2021; Srivastava, 2021; Xie & Zhu, 2020).

Methods of this study is not clear. The section of Materials and methods must be re-structured with following three sections only and same order:
- Sample and data
- Measures of variables
- Models and Data analysis procedure.

Response:
The research method has been adapted to the format recommended by the reviewer.

Results:
1. Authors have to avoid a lot of subheadings that create fragmentation and confusion. If necessary, can use bullet points (same comments for section of results and all sections).

Response:
They have been revised in accordance with the recommendations of the reviewers.

1. Figure 1 has to indicate the year in title to be clear.

Response:
The figure-1 is already added.

1. The two graphs can be merged in a single figure with two lines to be clear. Figures 2 and 3 are so small that cannot be read. Provide a note to the figure with more description. Or put them in appendix with larger size per each month.

Responses:

The figures have been revised according to the reviewer's recommendation.
An appendix of spatial maps per month for each wind speed variable has been created.

Discussion. First, authors have to synthesize the main results in a simple table to be clear for readers and then show what this study adds compared to other studies.

Responses:

It has been adjusted with the recommendations of the reviewers.
A table of main results has been added.

Conclusion so short it is useless. Conclusion has not to be a summary, but authors have to focus on manifold limitations of this study and provide suggestions of environmental, health, crisis management and social policy, as well as how nations can prevent with good environmental and health policies.

Responses:
The conclusions have been adjusted with recommendations from the reviewer:
Based on the overall results, areas with high wind speeds tended to show an increase in the number of COVID-19 cases, especially in the coastal areas of Jakarta. Wind speed plays a role in increasing the spread of SARS-CoV-2, in people who did not implement health protocols properly. Therefore, the impure cases are triggered by the rate of the wind-speed-based transmission. Consequently, strict health protocols (washing hands, wearing masks, physical distancing), applying maximum capacity, and regulating work hours in places with open-air conditions and crowd potentials, especially tourist attractions in areas with high wind speeds, including coastal regions, serve as a basis for consideration in policymaking.
The limitation of the study that did not include other influential factors, such as air pollution levels, economic wealth, cultural characteristics, etc., is important to be included in future research to provide policy makers with comprehensive recommendations from the perspective of environmental, socio-economic, and other factors, in order to create programs that can prevent similar pandemics in the future.

Thank you for all the input so this article can be even better.
With warm regards,
Authors

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Competing Interests

The authors do not have any competing of interests.

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Reviewer Report

17 Views

24 Mar 2023 | for Version 1

Edmund I. Yamba, Department of Meteorology and Climate Science, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana

17 Views Cite this report Responses(1)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Main research area is Biometeorology and Bioclimatology with current focus on weather and climate driven infectious diseases, extreme heat and human health, air pollution and respiratory infections, weather and climate extreme impacts, tropical climate variability, climate modelling and Urban Heat Island (UHI) warming,

Respond to this report

Responses (1)

Author Response

29 Nov 2023

Dewi Susanna, Department of Environmental Health, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia

Dear Dr. Edmund I. Yamba
Department of Meteorology and Climate Science, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Thank you very much for the valuable comments.
Here, we tried to respond your comments as follow:

The title vs the content presented:

Based on your interpretation, it appears that the authors seek to establish the association between wind speed and COVID-19 cases in Jakarta, without delving into causality, as evidenced by their utilization of Pearson's correlation analysis. The use of the term "effect" in the title implies a notion of causality. Nevertheless, the research work put forth does not explicitly establish causality, but rather investigates the existence of a relationship through inferences. It is recommended that the authors reconsider the phrasing of the title to reflect the content of their study. For example, something like “The relationship between wind speed and COVID-19 infections in Jakarta”.

Response:
Thank you for the proper title. The title of the article has been revised to "The relationship between wind speed and COVID-19 infections in Jakarta"

Introduction/novelty:

Despite the work's overall satisfactory and engaging presentation, the authors have not explicitly addressed the existing research gaps and their contributions to filling them. Previous studies have established the relationship between wind speed and the spread of COVID-19, which raises questions about the motivation behind the authors' replication of this work in Jakarta. Specifically, what were the compelling research questions that drove this inquiry and how were they answered? Hence, the originality or novelty of the work appears to be limited, and the authors should strive to enhance it. One suggestion for improvement could be to extend beyond the typical approach of establishing correlations between weather variables and COVID-19, which is prevalent in most existing literature. Merely identifying a correlation between two variables is insufficient to determine the causative relationship between them. Therefore, panel estimation techniques, for example, could be explored by the authors to investigate the causal aspect further.

Responses:
The previous research gaps and the researcher's contribution in filling the previous research gaps (novelty) have been added in the introduction section:
“In several countries, researchers have found that high wind speeds play a role in the increase of COVID-19 cases.^7–12 This is because SARS-CoV-2 can survive in the air, so wind speeds accelerate the spread of the virus by carrying droplets to various destinations.⁷
In the internal environment, higher wind speeds decrease the viral load indoors, which can prevent SARS outbreaks.¹⁴ Other researchers have found that high wind speeds can actually suppress the spread of COVID-19 cases.^15–17
This study considers some of the limitations of previous studies by examining the potential of wind speed in influencing the spread of cases based on regional characteristics^{7,9–12,15,17} because environmental conditions between regions are different.¹⁸ In addition, daily COVID-19 case data were converted into weekly cases to adjust for the incubation period of COVID-19 (5-6 days) and the uncertain time interval between the first day of infection and the day of sampling.¹⁹
As well as the potential spread of cases based on regional characteristics in DKI Jakarta, especially in coastal areas”.

Data collection:

The authors conducted a study that involved the collection of daily wind speed and COVID-19 case data spanning March to September 2020, which was then converted into weekly data for 31 weeks. The rationale for the conversion remains unclear, and it would be beneficial if the authors could elucidate their reasons for doing so and how this approach facilitated more effective analysis. Additionally, it is essential to understand how gaps in the data were managed as the quality of the data used is fundamental to the reliability of the results obtained. The authors reported that the study was conducted in Jakarta province, consisting of 261 urban villages. However, it is not evident whether the data used were obtained from each of these villages and aggregated for the province. If this was the case, it would be useful to know whether data were available for each village. It is recommended that the authors provide more comprehensive details regarding the availability and quality of the data used in their analysis to enhance the reliability of their findings.

Responses:

The reason for converting daily covid-19 case data into weekly covid-19 case data has been added to the data collection section:

The conversion of daily cases to weekly cases is related to several considerations, such as the incubation period of COVID-19, which lasts for 5–6 days on average, and the uncertain time interval between the first day of infection and the day of sampling.¹⁹

An explanation of how to obtain covid-19 case data in 261 villages has been adjusted in the data collection section:

These data were obtained from case reports of hospitals, health centers, and laboratories recorded per urban village by the Jakarta Provincial Health Office and can be accessed on the Jakarta Provincial Health Office website (https://corona.jakarta.go.id/id/data-pemantauan) and reports of two weather monitoring stations (Kemayoran station and Tanjung Priok station) in Jakarta, which can be accessed on the website of the Meteorology, Climatology, and Geophysics Agency (https://dataonline.bmkg.go.id/akses_data).

Statistical data analysis:

The authors employed Pearson's product-moment correlation test to assess the association between wind speed (independent variable) and COVID-19 cases (dependent variable). However, it is important to note that Pearson's correlation test necessitates the use of normally distributed data. As the daily COVID-19 and wind speed data were not normally distributed, the utilization of Pearson's approach may prove problematic. Therefore, it is imperative that the authors examine the normality of the daily or weekly data used to inform their selection of the appropriate correlation test. In this context, Spearman's correlation test may be suitable since it does not rely on the approximate normality of the data. Furthermore, the authors categorized the strengths of the correlation between wind speed and COVID-19 into four groups: absence/weak relationship (r = 0.00–0.25), moderate (r = 0.26–0.50), strong (r = 0.51–0.75), and very strong/perfect (r = 0.76–1.00). It is necessary to elucidate the scientific rationale for this division and the guidelines employed. This will enhance the clarity and reliability of the authors' findings and recommendations.

Responses:

An explanation of the use of the pearson-product moment test has been added to the statistical data analysis section:

Before the correlation test is applied, the data normality test is conducted first. The results of the normality analysis show that the maximum wind speed variable is normally distributed, while the average wind speed and COVID-19 cases are not normally distributed. From these results, variables that are not normally distributed are then transformed by the square root (x) to obtain normality. This step is taken to fulfil the requirements for multiple linear regression analysis (normality assumption).

The source of the correlation strength categorization has been provided and the reason for categorizing the correlation strength has been added:

In addition, the strengths of the association were qualitatively divided into four categories, where r = 0.00–0.25 was absence/weak relationship, r = 0.26–0.50 was moderate, r = 0.51–0.75 was strong and r = 0.76–1.00 was very strong/perfect.²⁰
This categorization aims to determine how strongly the independent variables (maximum wind speed and average wind speed) correlate with the dependent variable (COVID-19 cases). It is also used to discuss the results of the analysis in the discussion section.

Thank you for all the input so this article became more comprehensives and understandable for the readers.

View more View less

Competing Interests

We do not have any conflict of interests.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Analysis of the effect of wind speed in increasing the COVID-19 cases in Jakarta

Abstract

Keywords

Introduction

Methods

Study design

Data collection

Statistics data analysis

Spatial data analysis

Results

Statistical relationship analysis

Graphical relationship analysis/time trend

Figure 1. Wind speed (maximum and average) and COVID-19 cases per week in the Special Capital Region of Jakarta (accessed from: https://dataonline.bmkg.go.id/akses_data and https://corona.jakarta.go.id/id/data-pemantauan).

Spatial relationship analysis

Figure 2. Maximum wind speed spatial pattern with COVID-19 cases from March to September 2020 (accessed from: https://dataonline.bmkg.go.id/akses_data and https://riwayat-file-covid-19-dki-jakarta-jakartagis.hub.arcgis.com/).

Figure 3. Spatial pattern of average wind speed with COVID-19 cases from March to September 2020 (accessed from: https://dataonline.bmkg.go.id/akses_data and https://riwayat-file-covid-19-dki-jakarta-jakartagis.hub.arcgis.com/).

Discussion

Conclusions

Ethical approval

Data availability

Underlying data

Acknowledgments

References

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