The relationship between climate change and malaria in South-East Asia: A systematic review of the evidence

Background: Climatic change is an inescapable fact that implies alterations in seasons where weather occurrences have their schedules shift from the regular and magnitudes intensify to more extreme variations over a multi-year period. Southeast Asia is one of the many regions experiencing changes in climate and concurrently still has endemicities of malaria. Given that previous studies have suggested the influence of climate on malaria’s vector the Anopheles mosquitoes and parasite the Plasmodium group, this study was conducted to review the evidence of associations made between malaria cases and climatic variables in Southeast Asia throughout a multi-year period. Methods: Our systematic literature review was informed by the PRISMA guidelines and registered in PROSPERO: CRD42022301826 on 5 th February 2022. We searched for original articles in English and Indonesian that focused on the associations between climatic variables and malaria cases. Results: The initial identification stage resulted in 535 records of possible relevance and after abstract screening and eligibility assessment we included 19 research articles for the systematic review. Based on the reviewed articles, changing temperatures, precipitation, humidity and windspeed were considered for statistical association across a multi-year period and are correlated with malaria cases in various regions throughout Southeast Asia. Conclusions: According to the review of evidence, climatic variables that exhibited a statistically significant correlation with malaria cases include temperatures, precipitation, and humidity. The strength of each climatic variable varies across studies. Our systematic review of the limited evidence indicates that further research for the Southeast Asia region remains to be explored.


Introduction
Climatic change, as an inescapable fact, refers to the changes in long-term normal weather conditions.Unlike the varying weathers due to seasonality that is of common occurrence in one given year, the climate is indicated by weather pattern types and related classifications such as the Köppen-Geiger,which subdivides climates into the tropics, temperate, cold and polar. 1 The climate normal range is typically measured over a 30-year period but spans of 5-to 25-year periods have also recently been included. 2A changing climate therefore also implies alterations in seasons where weather occurrences have their schedules shift from the regular and magnitudes intensify to more extreme variations over a multi-year period.
Naturally, the climate system has always changed across different geological epochs since our blue planet formed approximately 4.6 billion years ago.The epochs, which are part of a system of chronological dating, so called by geologists, paleontologists and paleoclimatogists, represent periods of geologic history.The current period, known as the Holocene, began around 11,000 years ago following the end of the Pleistocene epoch.The Holocene is marked by a relatively stable climate with averaging global temperature variations of about plus or minus 1°C for every turn of the century so far. 3 This marked stability has been advantageous for humanity and has allowed the establishment of modern civilizations, beginning with the advent of agriculture that relied on the very stable climatic conditions of the early Holocene. 3 important feature of the current epoch is the natural greenhouse effect where the sun's energy is partially absorbed by the earth's surface and reflected back into space.The solar radiation is absorbed by naturally occurring greenhouse gas molecules which disperse the energy as heat thereby warming the lower atmosphere and conferring the needed energy and radiation to the biosphere.In the earth's normal state, greenhouse gases, which include carbon dioxide (CO 2 ), water vapour, nitrous oxides, and other compounds such as methane and ozone naturally exist around us. 3 However, with the ascent of industrialized civilization, humans have since caused atmospheric imbalances with increased accumulation of greenhouse gas emissions from anthropogenic activities.This has led to climatic variations and feedback, which include precipitation intensification as well as temperature rises. 4,5ese extreme feedbacks are of concern because they influence the reproduction of infectious agents such as viruses, bacteria and vectors such as mosquitoes and flies as they are sensitive to fluctuations in climatic variables such as temperature. 6In the event of climatic fluctuations that enable reproductive enhancement of infectious agents, the spread of disease amongst humans inevitably increases and overall public health conditions are threatened.An example of this is malaria, wherein climatic fluctuations have been shown to influence the risk factors posed by both the infectious parasitic agent, the Plasmodium group, and Anopheles mosquitoes. 7,8In fact, according to a study on malaria endemic Africa, optimum temperature window for malaria transmission is modelled to be in the 30-32°C range.The implication is that areas with a warming trend towards said range could potentially see persistent malaria endemicity. 9though malaria incidence and prevalence are also determined by variables like urbanization, globalization, migration patterns as well as land-use changes, 10 and the disease remains endemic in many parts of the world in spite of sociodemographic developments such as throughout Southeast Asia (WHO, 2020).In contrast to other malaria endemic regions such as sub-Saharan Africa, the particular attention to Southeast Asia is heeded on the fact that the region is home to a biodiverse array of malaria vectors 11 as well as dissimilar vector behaviors. 12erefore, with regards to the fact that malaria is a climate-dependent disease, it is endemic in many areas across Southeast Asia and the region having experienced climatic changes like the rest of the world, this study was conducted to critically assess and review the evidence of associations made between malaria cases and climatic variables in the region over a multi-year period, in line with the span of a climate normal range.

Search strategy
The conduct of this study was guided by the PRISMA 2020 checklist for review studies 13,14 and registered in PROSPERO: CRD42022301826 on 5 th February 2022.One Indonesian database (Garuda) and three international REVISED Amendments from Version 1 This revised version of the systematic review is built upon the substantial comments by the three peer reviewers, which we appreciate.Although no considerable changes that have been made, narrative tweaks in the introduction section and discussion section to better align with the suggested recommendations by peer reviewers so that the audience may understand the full scope of this study better in reading.Additional minor technical tweaks due to a typing error and miss formatting were also made, and we thank the reviewers for highlighting our article.
Any further responses from the reviewers can be found at the end of the article databases (PubMed, SpringerLink, ProQuest and Scopus) were searched to gather peer-reviewed articles for this review of evidence.The Indonesian database was included to capture additional articles catered in the authors' local language as previously done in Babaie et al. (2018) 15 with the inclusion of Persian databases for a review of Iran and Fischer et al.
(2020) 16 with the inclusion of German and French articles for a review of Europe.The search strategies applied were deployed in multiple sequences to mitigate any biases arising from missed articles from each database.The articles were then immediately disbursed amongst authors following selection.The keywords for our search included the terms 'climate', 'iklim', 'malaria' and 'Southeast Asia' or the 10 Southeast Asian countries searched as 'Indonesia', 'Malaysia', 'Singapore', 'Philippines', 'Thailand', 'Myanmar', 'Laos', 'Cambodia', 'Vietnam' and 'Brunei'.The search terms and methods along with the appropriate incorporation of truncations and operators specific to each database was discussed and consulted between DS and AR.Our search strategy did not include any limitations on publication periods.The search methods used and respective retrieved results from each database used are detailed as follows: • PubMed (169 retrieved results): (("climat*"[All Fields]) AND ("malaria"[All Fields]) AND ("Indonesia" OR "Malaysia" OR "Singapore" OR "Philippines" OR "Thailand" OR "Vietnam" OR "Laos" OR "Cambodia" OR "Myanmar" OR "Brunei" OR "Southeast Asia" [All Fields])) • SpringerLink (126 retrieved results): where the the title contains "climate" AND "malaria" AND ("Indonesia" OR "Malaysia" OR "Singapore" OR "Philippines" OR "Thailand" OR "Vietnam" OR "Laos" OR "Cambodia" OR "Myanmar" OR "Brunei" OR "Southeast Asia") with Content Type set to Articles • Garuda (10 retrieved results): "iklim" AND "malaria"

Inclusion and exclusion criteria
Original articles written in Indonesian and English which were analytical ecological studies and utilized longitudinal or time-series data of climatic variables with malaria incidence and/or prevalence in regions across Southeast Asia were included.Additionally, the articles included were studies which quantitatively analyzed the data through correlation, regression and/or mathematical models to infer relationships between multiple meteorological measures to reflect climate change and malaria incidence and/or prevalence.The studies that did not include data from a multi-year period and quantitative models with only one climatic variable were excluded.

Study selection
Three reviewers were involved in this systematic review of evidence with discussions and decisions conducted online.The selected papers were systematically reviewed thematically, and their methodologies assessed by AR with independent verifications by TF who also ensured no relevant articles were missing in the systematic review.Then, eligibility of the full-text records following screening of abstracts was conducted by AR and further corroborated by DS and TF.The three authors were familiar with reviewing and presenting descriptive assessments of quantitative results which should minimize the potential bias arising from reporting conflicting results.Any differing judgements on the selection of articles and extraction of results were resolved with the expert verdict of DS who also gave the final confirmation on credibility of the synthesis with regards to the climatic variables' influence on malaria dynamics.AR compiled the retrieved and summarized data of studies selected from the eligibility assessment stage into a separate review table in a shared Microsoft Word document (Version 2207 Build 16.0.15427.20182).DS and TF worked independently in corroborating the extracted data and narrative outlined in the shared review table.The systematic review flowchart to document our research process according to PRISMA conventions is shown in Figure 1.

Data extraction
Data was extracted from the studies that met the eligibility conditions.The studies were imported from their respective online databases and managed with the reference management software Zotero (Version 6.0.13,RRID: SCR_013784).The information included in the extractions were title, author and year of publication, location of study, period of study, independent and dependent variables, the scale and measure of the variables, quantative analysis methods, and summary of results.These extracted data were retrieved and summarized for qualitative synthesis within a table in a shared Microsoft Word document that was independently corroborated by the reviewing authors.

Data synthesis
The study utilizes a narrative synthesis method to describe the overarching influence of climatic variables indicated by meteorological measures such as temperature, humidity, wind speed and precipitation on malaria prevalence and/or incidence in countries across Southeast Asia.The strength and direction of the influence between variables is derived from the outcomes and summaries of the quantitative correlation, regression or mathematical models presented in the reviewed studies.Every climatic variable found throughout the review is described in the narrative synthesis irrespective of the number of studies that include them for the quantitative models.The reported outcomes of studies included in the final review are tabulated to group them according the the temperature, precipitation, humidity, and windspeed indicators as a meteorological proxy variables of climate change against their respective effects on malaria.Any incomplete reported outcomes in statistical tables found within the reviewed articles were thoroughly searched in the textual section of the articles with corroborations from the three authors.Categorized tabulations of the summarized articles were made using Microsoft Word's table features and shared amongst the three authors concurrently.

Results
During the first stage of the review process, a total of 535 records were identified from four different databases.10 records were written in Indonesian and 525 in English.We then conducted a deduplication process which then left 391 records for   The authors add that malaria cases associate positively with the preceding one-and two-month humidity levels but are reversed when associated with the levels of three months earlier.The correlation between the precipitation variable used and monthly malaria cases exhibit statistical significance (r = -0.431,p = 0.009).The author also used number of rainy days which showed a negative correlation (r = -0.349,p = 0.037).Variable: Mean monthly rainfall Method: Regression analysis and information value analysis (IV) In the regression analysis, rainfall against monthly malaria incidence showed statistical significance to be used in the information value analysis to determine malaria risk levels.
Results of the IV analysis shows that moderate risk areas are those with average monthly rainfall levels of 1500-2000 mm over wetlands, bare lands, and water bodies at elevation levels of 300-500 m.Meanwhile, high risk areas have average monthly rainfall levels of 400-900 mm and 900-1500 mm over land types such as agricultural area, forest area and urban areas with elevations of 100-300 m. abstract screening.After 391 of the articles were screened from their abstracts, 352 were excluded as they included studies outside the Southeast Asian region, were not ecological analyses or did not indicate the inclusion of more than one climatic variable.We were then left with 39 records for full-text eligibility assessment.In this final stage, 20 full-text articles were excluded as they did not include malaria cases as an outcome variable and did not use multi-year data for analysis in addition to the similar exclusion reasons of the previous stage.This left us with a total of 19 articles selected for the final review which we have summarized categorically and presented in Tables 1-4.8][19] However, due to insufficiencies in the reported statistical results and variations in The regression results of the study shows that every additional millimeter of precipitation leads to a 2.1% reduction in malaria cases (95% CrI = -2.3 to -1.9, p < 0.05).Correlation between yearly windspeed with API is not statistically significant.The authors argue that as the research was limited to yearly data, a more granular monthly data is recommended for better analysis of the rank-correlation between climatic variables and malaria incidence.
methodologies in the 19 selected articles, a meta-analysis report could not be made in this study as we've detailed in our added underlying data. 20This review is therefore only a narrative systematic review.

Temperature and malaria (19 studies)
Temperatures across different locations in Southeast Asia proved to be a worthy inclusion for analyzing the relationship with malaria cases based on the reviewed studies.2][23][24][25][26][27] The optimal temperature for Anopheline reproduction according to Mau et al. (2020) is between 25-27°C whilst the Plasmodium group's extrinsic cycle is optimum within the 20-30°C range. 22This implies that as temperatures become warmer within the optimum range, the duration of incubation is shortened, and mosquitoes become infective much sooner. 22,24,26at said, the quantitative analyses conducted in the reviewed studies exhibited varying results with temperature variables being averaged over a year, a month, split into maximum and minimum ranges as well as added lagged variations.For instance, in Kiang et al.'s (2006) neural network analysis, the composite use of mean monthly temperature with other climatic variables and vegetation index resulted in a model configuration to assess malaria cases with a training accuracy of 73% and testing accuracy of 53%.The model was developed based on variable data from 19 provinces across Thailand throughout a seven-year period (1994-2001).
Meanwhile, a study of the Koh Chang district in Thailand throughout 2001-2011 indicates that maximum temperature and mean temperature are positively correlated with malaria cases (r = 0.150 and r = 0.190 at α ≤ 0.01) by one year according to the count regression models. 29Similarly, Rejeki et al. (2018) also utilized count models in which maximum and minimum temperatures were included with the addition of lagging by one, two, three and 12 months.The results of their baseline Poisson model suggest that minimum and maximum temperatures have a significant influence on monthly malaria cases at the negative and positive directions respectively.However, after the inclusion of a dispersion parameter and testing for fit, a negative binomial model was selected in Rejeki et al. (2018) have offered their explanatory evidence through a non-linear thresholding effect with spline-fitted models that establish a peak positive effect of temperature at around 30°C. 29,30 Moreover, another alternative take on the temperature-malaria case relation is found in studies by Bui  found that temperature variables have district specific effects with many regions exhibiting opposite interactions with malaria cases, i.e., in some regions malaria cases soar as temperatures rise while others reduce with increased heat.The same is stated in Kotepui and Kotepui (2018) and Noppradit et al. (2021), where the latter argues that the lack of statistical significance between malaria and temperature variables in their study was due to topographic factors of the study site which were unfavorable to malaria from the commencement of the study.

Precipitation and malaria (19 studies)
The next climatic variable analyzed for its association with malaria in the reviewed studies was precipitation.[26] Across the 19 reviewed studies, the measures of precipitation as an independent variable varies.They defined the index as the product of the sum of precipitation and number of rainy days within a given month that is then divided by the total number of days in said month. 27e relationship between precipitation between malaria cases across the reviewed studies was inconclusive as the resultsof some studies did not exhibit statistical significance for the variable. 30,32,33,35,38The study by Suwito et al. (2010)  did not statistically associate precipitation index with malaria cases, but their resulting correlation between the former with Anopheles density measured by man-biting rate (MBR) exhibited a positive relationship that was statistically significant.As for direct associations with statistical significance, the correlation and linear regression results in Mau  et al. (2020) showed that yearly precipitation has a positively linear relationship with API where their linear regression model implies that for every 1 mm increase in precipitation, API cases increase by 0.028.The negative binomial regression results in Rejeki et al. (2018) also indicate that a 1 mm increase in precipitation has a positive influence on malaria cases by 0.08% and 0.09% with the use of three-month and 12-month lags respectively.However, in contrast to the three previous studies, results in Jubaidi (2015) showcases negative correlations that are statistically significant between monthly precipitation and number of rainy days with monthly malaria incidence (r = -0.431,α ≤ 0.01 and r = -0.349,α ≤ 0.05 respectively).Taking into account the previously mentioned studies, the results in Jubaidi (2015)  indicate the need to take precipitation as a lagging indicator for direct associations with malaria cases as suggested in  [39][40][41] Humidity and malaria (14 studies) As previously mentioned, humidity as a meteorological measure of climate is an important indicator for malaria cases as it enables the lifespan of an infective adult Anopheles mosquitowhere relative humidities of at least 60% and above optimize the mosquitoes' activity to bite and infect. 23,24,26,27ly seven of the reviewed studies had results where humidity was a statistically significant independent variable.That said, the results were inconclusive.In the negative binomial model of Rejekti et al. (2018), results suggest that a 1% increase in maximum relative humidity is associated with a 10.47% decrease in malaria cases after two months.However, the results of Suwito et al. (2010) instead further validate the influence of humidity on Anopheles activity to bite and infect as average humidity is shown to have a statistically significant positive correlation with MBR.Narrative background for humidity posed by the other reviewed studies, where previous prevailing studies are also referred to, would suggest the latter study as being the sounder evidence for humidity's relationship with malaria cases (albeit indirectly).The authors in the former study unfortunately did not provide any theoretical explanations as to why their two-month lagged maximum humidity was associated with a decrease in malaria cases.However, an argument could be made regarding the range of the maximum humidity throughout the period and location of study.In Purworejo, the maximum humidity between 2005-2014 varied between 83-99%, which is well beyond the 60% necessary optimum for Anopheles mosquito activity.Alternatively, the results in Rejeki et al. (2018) could suggest that the use of humidity as a lagged indicator associated with malaria cases is unwarranted.

Windspeed and malaria (Two studies)
Only two of the reviewed articles included windspeed as a climatic variable that was assessed for its relationship with malaria cases. 21,26Based on references included in their article, Sandy and Wike (2019) state that windspeed has an influence on Anopheles mosquitoes' flight range, hence enabling an expanded scope of humans to bite.That said, the evidence in both Jubaidi (2015) and Sandy and Wike (2019) indicate that the resulting correlation between windspeed with monthly malaria incidence and API respectively did not exhibit statistical significance.The authors in both attribute the narrow windspeed range across the periods and their respective locations of study as the potential reason for the variable not resulting in statistically significant correlations.

Conclusions
Following previous systematic reviews of evidence on changing climatic variables' relationship with malaria for a given region such as Babaie et al. (2018), 15 Fischer et al. (2020), 16 and Bai et al. (2013), 42 this review finds that changing temperatures, precipitation and humidity across a multi-year period are correlated with malaria cases in various regions throughout Southeast Asia.The established evidence, however, was only limited to 19 articles with most studies in Indonesia (7), Vietnam (3) and Thailand (7).Many other studies were also excluded from this review as they either utilized only a single meteorological measure, which undercuts the complex dynamics of climatic variables or claimed to assess changing climatic variables despite only analyzing a single-year period, which is not informative for exhibiting a change of climate normals that is indicated by multi-year averages.
However, the exhibited evidence for the case of Southeast Asia suggests that further explorations could still be made with regards to the intricate dynamics of changing climatic variables with malaria incidence and/or prevalence across the region.]38 In conclusion, the findings of this systematic review of evidence could serve to inform the environmental ministries and health ministries of the respective Southeast Asian countries for climate change adaptation and malaria elimination strategies amidst climatic exacerbations.

Data availability
Underlying data Figshare: Underlying data for 'The relationship between climate change and malaria in South-East Asia: A systematic review of the evidence'.https://doi.org/10.6084/m9.figshare.20697298.v1. 20porting guidelines Figshare: PRISMA checklist for 'The relationship between climate change and malaria in South-East Asia: A systematic review of the evidence'.https://doi.org/10.6084/m9.figshare.20489235.v1. 13ta are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
enough background information to inform the reader on how similar or different the countries are in relation to climatic variables and malaria i.e. trends over time, so that its clear to the reader that all countries are similar.The changes in weather conditions do not operate in isolation, therefore this being a review, I suggest you consider including all other variables that might have contributed to the changes in malaria transmission in the region, so that you provide an overall picture of what exactly is going on.
Here are some comments and questions for your consideration.
The authors need to define what the term "climate" really refers to in their study.
I would suggest the term "climate variability" rather than climate change in the title.I realize it's the variations in the climatic variables that is leading to the changes in malaria transmission. 1.
SEA region comprises of 10 countries, but the review focuses on 19 studies that only cover 3 countries on the SEA region.My concern is are the 3 countries a good representation of the whole region?If yes, where is the evidence suggesting that indeed the 3 represents the whole region with respect to the subject matter i.e. climate? 2.
In view of the same concerns raised above.Malaria transmission and mosquito vectors are highly dependent on the local conditions, which in turn lead to modifications of the local weather conditions that ultimately translate to variations in climatic variables, a) There is no mention on the specific vectors responsible for malaria transmission in the 10 countries b) What are their climatic requirements in in terms of malaria transmission?c) Are the conditions for malaria transmission similar in all the 10 countries?d) Even within the same country, transmission is never uniform e.g.northern part of Indonesia might be different from southern part with respect to malaria.Local conditions, human activities are the other main drivers.This information is missing, and yet its what makes malaria transmission completely heterogenous.

3.
The reader needs to be informed of any assumptions that the authors might have put into consideration.

4.
The concerns raised should also inform you on the recommendations for future studies that are looking at climate variability and its effects on vector borne disease transmission.

5.
Consider revisiting the conclusion of the study and state clearly, how helpful climate data would benefit the environment and health ministries?And what are the specific recommendations to the SEA countries in terms of climate adaptation and malaria elimination?

6.
Are the rationale for, and objectives of, the Systematic Review clearly stated?

Are sufficient details of the methods and analysis provided to allow replication by others? Yes
Is the statistical analysis and its interpretation appropriate?Partly Are the conclusions drawn adequately supported by the results presented in the review?Partly Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Medical entomologist interested in malaria vector ecology, control and public health in general 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.

Eleanore Sternberg
Tropical Health, London, England, UK I would simplify the writing and refocus the introduction on malaria.A brief history of the world's climate is less relevant than covering some of the current literature on climate and malaria (and other vector-borne diseases).There is a substantial body of literature focused on climate change and malaria in Africa that would be relevant, plus modelling results on climate change and infectious disease.
○ "This implies that as temperatures become warmer within the optimum range, the duration of incubation is shortened, and mosquitoes become infective much sooner" in the discussion is an important point.Once the temperature exceeds the optimal temperature (as it will undoubtedly in some places), that will slow or stop plasmodium development.This could potentially explain why increasing temperature is associated with more malaria in some instances and not in others.

○
The results and discussion focuses on significant associations between malaria and climate variables, but the key result would be change over time.Change over time is mentioned in the conclusion, but I don't think it's clear in the way that results are presented whether the papers that are discussed showed temporal change as well as associations between malaria and climate indicators.Also related to change over time, studies that cover a long time period (e.g.reference 30 covers 1982 -2018) are going to be more informative than studies that only cover a few years (e.g reference 18 that covers 2011-2013).The authors could consider highlighting the papers with long study periods in some way, for example by presenting them first or going into a bit more detail into their results.Reviewer Expertise: Vector-borne diseases 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.
authors carefully consider this and include the suggestions in the revision.The need for the present review or the rationale for conducting the review is not clearly explained. 1.
Though the inclusion and exclusion criteria are well defined, is the search strategy is comprehensive and transparent? 2.
Is there any assessment of the risk of bias in the included studies? 3.
It would have been better if the authors have included the recent and up-to-date literature.All the papers cited in the review used only the statistical methods but there is recent literature which has used machine learning and deep learning models to assess the relationship between climatic factors and malaria cases.

4.
Please mention Plasmodium in italic throughout the manuscript.5.
In the result section, the number of records written in English is misquoted as 425, please change it to 525.

6.
The review considered the studies conducted only in 3 countries and does not provide a balanced view of the topic.

7.
Though numerous climatic variables influence malaria transmission, the study considered only temperature, precipitation, humidity and wind speed.The inclusion of the majority of the climate variables would provide a better insight into the situation.

8.
Since all the climatic parameters exhibited variation in terms of significance in different regions, the discussion can be improved by mentioning major contributing factors in the specific region.

9.
Please mention the consequences of the findings in the conclusion section in the context of climate change and malaria?10.

Are the conclusions drawn adequately supported by the results presented in the review? Yes
Competing Interests: No competing interests were disclosed.

Figure 1 .
Figure 1.PRISMA review flowchart.45 (2018) in which maximum and minimum temperatures exhibited no significance at α ≤ 0.05.InMau et al. (2020), temperature was also shown to have a significant influence on malaria cases in their linear regression model.The difference being those temperatures were averaged over a year and negatively influenced malaria cases, which were measured as annual parasite incidence (API).The results inRejeki et al. (2018) and Mau et al. (2020) suggest that further increases of temperatures beyond certain levels will be associated with reductions in malaria casesin line with the optimum range for both Anopheline reproduction and Plasmodia incubation.In the Rejeki et al. (2018) Purworejo study, maximum temperatures recorded in the study period ranged between 28-30°C, 24 while the average temperatures between 2013-2019 at the study site in Mau et al. (2020) ranged between 25.13-27.58°C(which is already the optimum levels for Anopheline reproduction).Interestingly, the studies of Ninphanomchai et al. (2014) and Servadio et al.

Reviewer Report 10
July 2023 https://doi.org/10.5256/f1000research.137586.r176897© 2023 Sternberg E. This is an open access peer review report 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.

○
Are the rationale for, and objectives of, the Systematic Review clearly stated?PartlyAre sufficient details of the methods and analysis provided to allow replication by others?YesIs the statistical analysis and its interpretation appropriate?Not applicableAre the conclusions drawn adequately supported by the results presented in the review?PartlyCompeting Interests: No competing interests were disclosed.

Table 1 .
Summary of published articles assessing the association between temperature with malaria cases in various locations throughout Southeast Asia.

Table 2 .
Summary of published articles assessing the association between humidity with malaria cases in various locations throughout Southeast Asia.

Table 3 .
Summary of published articles assessing the association between precipitation with malaria cases in various locations throughout Southeast Asia.

Table 4 .
Summary of published articles assessing the association between windspeed with malaria cases in various locations throughout Southeast Asia.