The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review

Background: Climate change poses a real challenge and has contributed to causing the emergence and re-emergence of many communicable diseases of public health importance. Here, we reviewed scientific studies on the relationship between meteorological factors and the occurrence of dengue, malaria, cholera, and leptospirosis, and synthesized the key findings on communicable disease projection in the event of global warming. Method: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow checklist. Four databases (Web of Science, Ovid MEDLINE, Scopus, EBSCOhost) were searched for articles published from 2005 to 2020. The eligible articles were evaluated using a modified scale of a checklist designed for assessing the quality of ecological studies. Results: A total of 38 studies were included in the review. Precipitation and temperature were most frequently associated with the selected climate-sensitive communicable diseases. A climate change scenario simulation projected that dengue, malaria, and cholera incidence would increase based on regional climate responses. Conclusion: Precipitation and temperature are important meteorological factors that influence the incidence of climate-sensitive communicable diseases. Future studies need to consider more determinants affecting precipitation and temperature fluctuations for better simulation and prediction of the incidence of climate-sensitive communicable diseases.


Introduction
In the last decades, the global burden of disease has shifted from communicable to non-communicable causes [1]. The coronavirus disease 2019 (COVID-19) pandemic on the other hand has demonstrated how communicable disease remains a significant threat to global health, particularly as the climate crisis continues to influence disease spread in a variety of ways. The evidence shows that the global surface temperature during the most recent decade (2011-2020) was 1.09 [0.95 to 1.20] • C higher relative to the preindustrial period (1850-1900), which was driven by human activities [2]. Based on the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, the global surface temperature will continue to rise, ranging from 1.5 to 4.4 • C in the twenty-first century. The global warming of 2 • C is likely to occur in the mid-term period (2041)(2042)(2043)(2044)(2045)(2046)(2047)(2048)(2049)(2050)(2051)(2052)(2053)(2054)(2055)(2056)(2057)(2058)(2059)(2060) under the high greenhouse gases emissions scenario (Shared Socioeconomic Pathways, SSP3-7.0) [2]. A warming of 2 • C and more poses greater risk to human health [3], particularly on vulnerable subpopulations such as the elderly, low-income populations, and people with comorbidities [4,5].
Climate change poses a real challenge to public health and has contributed to causing the emergence and re-emergence of many communicable diseases of public health impor- 4 of 22 frame, (5) statistical analysis and climate model, (6) findings related to meteorological factors and climate change prediction, and (7) adjustment for confounding and crossvalidation. Figure 1 shows the PRISMA flow diagram. All of the studies that were chosen followed an ecological design. Due to nature of the data, which is too heterogenous in term of statistically methods, study outcomes, and settings, the quantitative synthesis and analysis was not carried out. on 11 items, with a maximum overall score of 15 points. Supplementary Materials S2 presents the assessment scale adapted from Dufault and Klar [24]. The article q was graded as low-(≤5 points), medium-(6-10 points), and high-relevance (≥11 po 2. 6

. Data Extraction and Synthesis
MB and NA extracted the data independently using a standardized data extr form and organized it in a standard Microsoft Excel 2019 spreadsheet. The da information collected included: (1) authors, (2) year of publication (3) country, (4 frame, (5) statistical analysis and climate model, (6) findings related to meteoro factors and climate change prediction, and (7) adjustment for confounding and validation. Figure 1 shows the PRISMA flow diagram. All of the studies that were c followed an ecological design. Due to nature of the data, which is too heterogen term of statistically methods, study outcomes, and settings, the quantitative synthes analysis was not carried out.

Background of the Eligible Studies
A total of 38 studies were included in this systematic review. Table 1  Records excluded (n = 585) Reports sought for retrieval (n = 81) Reports not retrieved (n = 0) Reports assessed for eligibility (n = 81) Reports excluded: (n = 43) Spatial distribution (20) Different study outcome (14) Vector distribution (9) Studies included in review (n = 38) Identification Screening Included Figure 1. PRISMA flow diagram.

Background of the Eligible Studies
A total of 38 studies were included in this systematic review. Table 1 shows a descriptive summary of the included studies. The 38 studies were conducted in Bangladesh, Brazil, China, Indonesia, India, Iran, Korea, Malaysia, Mexico, Nepal, Nigeria, Philippines, Puerto Rico, Sri Lanka, Singapore, Sudan, Taiwan, Tanzania, Thailand, and Vietnam. When categorized into World Health Organization (WHO) regions, the majority of the studies had been conducted in the Western Pacific Region (WEPRO) and South-East Asia Region (SEARO). The analyzed articles were published between 2007 and 2020. More than half of the studies (60.5%) were conducted between 2015 and 2020. The study time frame varied from ≤5 years (10.5%) to 6-10 years (52.6%). Most of the studies explored the association of meteorological factors with vector-borne diseases: 23 studies (60.5%) focused on dengue and 11 studies (29%) focused on malaria as the health outcome. Three studies and one study examined the impact of meteorological factors on cholera (7.9%) and leptospirosis (2.6%), respectively. Table 2 shows the characteristics of the included studies in terms of  statistical analysis and climate model used, the association between meteorological factors  and communicable disease incidence, target outcome, future prediction, adjustment for confounding factors, validation, and quality appraisal scoring.

Meteorological Factor Variables
The meteorological factor variables used in the included studies mainly consisted of rainfall/precipitation (34 studies), average/minimum/maximum temperature (33 studies), relative humidity (20 studies), and wind properties (three studies). Of these four meteorological factors, precipitation was most frequently associated with the selected climate-sensitive communicable diseases, followed by temperature, relative humidity, and wind properties. Of 23 studies reporting on dengue incidence, more than half reported a positive association between temperature (n = 17, 74%) and precipitation (n = 16, 70%) with dengue incidence. Furthermore, ten studies (43.5%) and three studies (13%) reported a positive association between relative humidity and wind properties, respectively, with dengue incidence. For malaria incidence, the majority of the studies reported a positive association with temperature (ten studies, 91%) and precipitation (nine studies, 81.8%). For the association between relative humidity and malaria incidence, five studies (45.5%) reported a positive association and three studies (27.3%) reported a negative association. No study reported an association between wind properties with malaria incidence. Two of three studies showed a positive association between temperature and precipitation with cholera cases. Additionally, Magny et al. [59] reported a positive association between chlorophyll A anomaly with cholera cases, while Reyburn et al. [60] reported a negative association. Lastly, Dhewantara et al. reported a positive association between temperature and precipitation with leptospirosis [61]. Table 3 summarizes the meteorological factors associated with the selected climate-sensitive communicable diseases. The other covariates included in the studies were duration of sunshine, El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), sea surface height (SSH), sea surface temperature (SST), sea level pressure, ocean chlorophyll concentration (OCC), and average river level. Some of the studies also included covariates related to landscape, such as normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI), as well as covariates related to socio-economics, such as piped-water access, human migration, and population growth.

Projection of Climate-Sensitive Communicable Diseases
For the projection of dengue incidence in the event of climate change, Colon-Gonzales et al. reported that, under three climate scenarios (A1B, A2, B1), the mean annual dengue incidence across Mexico would increase around 12-18% by 2030, 22-31% by 2050, and 33-42% by 2080 [30]. A similar study conducted in Dhaka, Bangladesh, reported that dengue incidence would increase by 1.5 times if ambient temperatures increased by 1 • C in 2100 relative to 2010. If the temperature increases by 2 • C, the incidence of dengue would increase by seven times, and the worst-case scenario of a 3.3 • C rise would increase dengue incidence in Dhaka by 43 times in 2100 relative to 2010 [27]. In Guangzhou, China, Li et al. reported that under climate scenario Representative Concentration Pathway (RCP) 2.6, the overall incidence of dengue fever would be low, as would the occurrence of high numbers of cases. However, the overall incidence and the occurrence of high numbers of cases would increase under climate scenario RCP8.5 [37].
For malaria projection, Kwak et al. reported a gradually increased trend of malaria in Korea during a simulation using the RCP4.5 climate change scenario and the CNCM3 climate model. The maximum occurrence shifted from August (during 2010-2011) to July (using simulation data of 2011-2100). In the future, malaria occurrence would continually increase between April and July (before the rainy season in the summer) compared to between June and August in 2010-2011 [52]. Asadgol et al. reported that, under RCP8.5, the trend of cholera cases in Iran would increase by 2050. For the next 30 years, the seasonal pattern of cholera will change and the highest cases will be observed during spring and summer. The average monthly cholera cases will be highest in August if compared to the baseline data [11].

Critical Appraisal of the Studies
The studies were appraised using a modified scale of a checklist for assessing ecological research quality [24]. None of the studies were scored as low relevance: 20 studies (52.6%) and 18 studies (47.4%) were scored as high and medium relevance on the assess-ment scale. For level of data aggregation, 8 (21%) studies were conducted at the national level, 11 (29%) conducted at regional or state level, and the remaining 19 (50%) involved province, county, district, or city as population unit. Only two (5.3%) studies used basic Spearman's rank and Pearson correlation as analytical methods. The rest of the studies applied advance statistical analysis such as Linear regression or Poisson regression, Autoregressive integrated moving average (ARIMA), and Multilevel Distributed Lag Non-linear Model (MDLNM). A total of 27 (71%) studies were conducted with a proper adjustment for covariates as suggested for ecological studies. For "quality of reporting", only eight (38%) studies include a statement of ecological study design. However, 13 (34.2%) studies explicitly justified study design, and most (97.4%) of the studies discussed the risk of ecological bias. The justification of study design allows readers to understand the rationale of choosing the design and apply the ecologic analysis. The risk of ecological bias needs to be explained clearly so that the authors sufficiently caution the readers in interpreting the results, representing the aggregated level. Table 4 presents the scores of modified scale for each quality assessment item adapted from Dufault and Klar [24].

Discussion
The aim of the present systematic review was to summarize key findings related to the relationship between meteorological factors and the occurrence of dengue, malaria, cholera, and leptospirosis, and to review recent communicable disease projection in the event of global warming. The present systematic review of 38 publications demonstrates that dengue, malaria, cholera, and leptospirosis transmission can be influenced by meteorological variables such as temperature, precipitation, relative humidity, and wind properties. The actions of these climate variables in influencing the transmission of communicable diseases are rarely independent. The combinations of a few climatic variables appear to be related to climatological niches for optimal disease transmission [62]. Although the effects of climate change have been observed worldwide, the extent and patterns of the effect differ based on the country's location and socio-economic conditions [6].
The present review shows that both precipitation and temperature are the most important meteorological factors for climate-sensitive communicable diseases, especially dengue and malaria. Several studies of different ecological units varying from city [37,39,45], province [31], regional [26,29,40], to national [41,47] levels have demonstrated a positive association between temperature and precipitation with dengue incidence. This result is not limited to studies conducted in countries with tropical climates, but also includes studies conducted in Guangzhou, which has a subtropical monsoon climate [37,45]. However, meteorological factors do not directly influence the incidence of dengue. Instead, meteorological variables such as temperatures, rainfall, and relative humidity have a direct impact on the larval development period, larval and adult mosquito survival, and the duration of the gonotrophic cycle of the primary dengue vector, and affect the general activity of the dengue vector, including host-seeking and blood meal intake [63].

Relationship between Metreological Factors and Dengue
The ambient temperature alters the vector population dynamic by affecting the development of immature stages and reproductive behavior [64]. The ideal temperature for Aedes aegypti development is between 22 and 32°C, while that for the A. aegypti adult lifespan and fecundity is between 22 and 28°C [65]. Increasing the temperature will shorten the egg-laying time of A. aegypti, thereby increasing egg quantity [66]. Moreover, higher temperatures are a favorable survival range for the vector and reduce the extrinsic incubation periods of the dengue virus. This will result in higher rates of viral transmission that can lead to increased dengue incidence [67]. On the other hand, three studies have demonstrated a negative association between temperature and dengue [33,35] and dengue haemorrhagic fever (DHF) [43]. According to Duarte et al. [33], the monthly incidence of dengue will decrease by 32% with every 1 • C increment in the monthly average maximum temperature. This effect is particularly for increasing the maximum temperature to >32 • C, which is higher than the temperatures considered optimum for the vector. These temperatures help hasten the evaporation and drying of wastewater distributed around the city that would otherwise create mosquito breeding grounds.
Wind speed and direction are also important climatic factors. According to Chumpu et al. the best-fit model of Phayao province, Thailand, which incorporated wind direction and wind power, showed the highest dengue occurrences at wind speeds of 5-6 knots. This indicates that wind power is crucial for the spreading of dengue by mosquitoes. A higher wind power may affect dengue fever cases. More wind power on the sea surface results in a greater evaporation zone. Adult mosquitos may be able to survive longer and spread dengue as a result of the increased humidity. For mountainous areas, the most significant meteorological factors are wind direction variables [31]. On the other hand, two studies conducted in the central region in Malaysia [29] and in Guangzhou [45] found that strong wind may suppress mosquito host-seeking activity and consequently reduce dengue transmission risk. However, only three studies included utilizing the wind properties as the independent variables. Future studies are recommended to explore wind properties as a possible meteorological factor related to dengue to overcome this limitation.

Relationship between Metreological Factors and Malaria
Additionally, the majority of the 11 studies on malaria included in the present review reported a positive association between temperature and precipitation with malaria incidence [48][49][50][51][52]55,56,58]. According to the data, increases in temperature, humidity, and rainfall facilitate the proliferation of mosquito populations at high altitudes. This expands the geographical distribution of malaria, allowing it to spread to new areas where mosquito populations previously did not exist. Furthermore, rising temperatures at lower altitudes, where mosquitoes and malaria are already endemic, alter the development cycle of the parasite that causes the Anopheles mosquito to transmit the disease, allowing it to develop malaria faster and therefore raising transmission rates [9]. Other than climatic factors, factors such as human migration, population growth, and deforestation are associated with malaria transmission. The relative contribution of these factors may vary between countries and regions. Furthermore, malaria transmission can be exacerbated by human behavior, such as actively storing water in open containers, routine outdoor socializing during peak hours of Anopheles biting time (dawn and dusk), and other activities such as farming and fishing, which may increase the risk of exposure to mosquitoes and malaria infection.

Relationship between Metreological Factors with Cholera and Leptospirosis
There is substantial evidence [11] that cholera infection is linked to meteorological factors, such as low precipitation and high temperatures during the summer months, which might facilitate bacterial reproduction and increase cholera incidence. However, in Zanzibar, East Africa, Reyburn et al. [60] reported that a 1 • C increase (at a four-month lag) would lead to two-fold increased cholera cases. Meanwhile, an increase of 200 mm rainfall (at a two-month lag) might increase cholera cases by 1.6-fold. Interestingly, a study in Matlab, Bangladesh, demonstrated a statistically significant one-month lag between OCC anomaly and cholera cases. Therefore, ocean and climatic trends are good predictors of cholera epidemics [59]. For leptospirosis, a study in China has shown that land surface temperature and rainfall are significantly associated with leptospirosis notification [61]. Warm temperature aids leptospire survival in the environment [71,72]. Hot weather encourages some activities, such as people and animals swimming in the same pool of water, e.g., rivers. Besides, high humidity is a favorable condition for leptospire survival [73]; however, Dhewantara et al. [61] did not include relative humidity as one of the covariates.

Projection of Selected Climate-Senstive Communicable Disease
The association between human activities and climate change has drawn increasing attention in recent years. It has been confirmed that human activity has a significant impact on present global warming. The rising emission of greenhouse gases has led to global warming and climate change, which have had various impacts, including on health, particularly toward communicable diseases [74]. Projection of the geographic distribution of A. aegypti and A. albopictus has revealed that the abundance of mosquitoes will increase by the 2030s and beyond compared to the present, suggesting that more individuals will be at risk of dengue fever [7,75,76]. A few studies have projected that exposure to the Aedes mosquito and the Aedes-transmitted virus would increase with 1.5/2.0 • C global warming [76,77]. For example, most of the tropics are now ideal for virus transmission year-round for both Aedes aegypti and Aedes albopictus, with suitability decreasing along latitudinal gradients. However, projected warming temperatures in 2050 will substantially increase the potential for year-round transmission in the tropics, even into previously protected high-elevation locations. In addition, many temperate regions are presently devoid of significant Aedes vectors. However, in 2050, the risk of Ae. albopictus transmission is projected to increase significantly in temperate countries, particularly in high-latitude portions of Eurasia and North America [75].
Elementary modelling predicts that the rising of global temperatures would increase the rate of malaria transmission and expand its geographical distribution [78][79][80][81]. Several studies have reported that the increased malaria transmission [82] or its re-emergence [83] is associated with global warming. Khormi and Kumar projected that the southern regions of China might become susceptible to malaria mosquito infection in the future, in which suitability is expected to increase. Anopheles would be able to survive in large regions of southern China that are now unsuitable or marginal [82]. Due to the high population density in these highly suitable areas, the number of individuals exposed to the Anopheles mosquito and hence to malaria is considerably increased. The predictive results indicate that modelling aids understanding of the disease transmission mechanism and assists in communicable disease intervention and control programs. However, the fundamental challenge for predicting climate-sensitive communicable disease transmission is how future climates can be best modelled at regional and/or local level. In other words, how can the results of global climate models (GCM) be suitably downscaled to a regional and/or local level? The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) describes different climate futures, all of which are considered possible depending on the volume of greenhouse gases emitted in the years to come. These scenarios are categorized into four classes: RCP2.6, RCP4.5, RCP6, and RCP8.5, labelled after a possible range of radiative forcing values in 2100. These scenarios can be used to project future climates based on GCM [84].

Strenghts and Limitations
This review highlights the current public health issues on climate-sensitive communicable disease. All papers included in this review had undergone systematic critical appraisal using an adapted appraisal tool suitable for ecological study design, as described by Dufault and Klar [24]. Thus, we used and adapted the structured assessment scale reported in other ecological review studies [85,86]. None of the papers included in this review were low relevance, as based on the quality appraisal score. However, we recommend caution when estimating the relationships between climate variables and dengue in the following aspects: use of time lags, analysis of extreme climatic events, differences between seasonal and long-term trends, nonlinear effects and threshold effects in the associations. In addition, there should be more emphasis on data quality and the use of information for decision making.
One limitation of this review is the included articles related to leptospirosis and cholera are limited. Therefore, caution is advised for interpretation when utilizing findings related to leptospirosis and cholera. Another limitation is that very few studies use the IPCC standardized climate change scenarios to predict future dengue, malaria, and cholera incidence. Besides, the exclusion criteria of non-English language articles could be one of the limitations of this review. Nearly half of the included studies were from WE-PRO, which majority comprises of non-English speaking countries. Therefore, this review might miss the wealth of related literature in particular published in Chinese. Due to the "English-language bias", this review could have bias estimates of effect, therefore reduce its generalizability. However, including studies published in non-English language may pose additional resources with respect to cost, time, and non-English language proficiency.
One of the strengths of using the IPCC AR5 climate model is its ability to predict climates over a longer time or glacial year. The disadvantage is that it only considers the natural Earth systems and not the interaction between humans and nature. Furthermore, most of the individual studies assumed that human hosts are immobile. However, mass migration may contribute to dengue and malaria infection dynamics, especially at scales that exceed the limits of mosquito dispersal [50].
We recommend that for future research to better understand dengue, malaria, cholera, and leptospirosis ecology to be directed at predicting the climate-biological relationships on disease transmission. Uncertainties due to confounding effects of urbanization, population growth, and human migration are required to develop scenarios based on future projections of population growth and socio-economic development, including human behavior. Future projection of climate-sensitive communicable diseases is greatly essential to aid planning and mitigation strategies by stakeholders, hence the need for scientific consensus on data potentially used in modelling.

Conclusions
This review provides robust evidence of an association between meteorological factors and the incidence of climate-sensitive communicable diseases, i.e., dengue, malaria, cholera, and leptospirosis. Precipitation and temperature are important meteorological factors that influence the incidence of climate-sensitive communicable diseases. Future studies need to consider more determinants affecting precipitation and temperature fluctuations for better simulation and prediction of the incidence of climate-sensitive communicable diseases. In addition to future forecasts, accounting for alternative climate factor variables, considering climate change scenarios and other non-climatic drivers such as the presence/absence of dengue and malaria vectors, human migration, population growth, and socio-economics as crucial factors triggering communicable disease transmission would be beneficial. This would strengthen projection realism and act as a platform for academic and policymaker consensus on provisions to mitigate future climate-sensitive communicable diseases incidence.