Exploratory analysis of the correlation between precipitation and storms and malaria prevalence at the national scale in Mozambique

Exploratory analysis of the correlation between precipitation and storms and malaria prevalence at the national scale in Mozambique | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploratory analysis of the correlation between precipitation and storms and malaria prevalence at the national scale in Mozambique Hengcheng Zhu, Lin Zhang, Euna Khan, Jaideep Srivastava, Mitchel Croal, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7755148/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Climate change is impacting the seasonal weather trends. Mozambique is one country that has been experiencing significant impacts of climate change. These impacts include changing weather patterns and more frequent and intense storms. Mozambique also has the 5th highest malaria prevalence globally. These storms have all impacted the efficiency of the Mozambique National Malaria Control Program to achieve their goals. The goal of our study was to determine the appropriate temporal lag between high precipitation events and malaria risk in Mozambique. This knowledge is imperative to understand when and how to best respond the impacts of these events. Methods Malaria monthly case count data at the district level were provided by the Mozambique National Malaria Control Program. Precipitation data were obtained from NASA GIS DISC Earth Data and were spatially and temporally aggregated to the district to match the malaria surveillance data. We investigated the correlation between precipitation and malaria-related values at the district level each month with the spatial and temporal correlations considered. We used Pearson correlation coefficients to quantify the correlations. We used Poisson generalized linear mixed models to determine the relative risk of malaria associated with precipitation nationally and for each of the three regions in Mozambique. Results We found evidence of strong spatial variation in malaria cases, malaria hospitalizations, and malaria deaths. There was evidence of a clear temporal relationship throughout Mozambique. Nationally, there was an increased relative risk for malaria at an 8-week temporal lag. However, this varied when stratified by region. In the Northern Region there was an increased relative risk for malaria between 6-week and 8-week temporal lags; in the Central region there was an increased relative risk for malaria between 10-week and 12-week temporal lags; and in the Southern region there was an increased relative risk for malaria between 10-week and 13-week temporal lags. Conclusion With increasing storms and changing weather patterns due to climate change there is a need to understand the specifics of the association between elevated precipitation and malaria risk. We found important spatial heterogeneity in temporal lag times between precipitation events and malaria risk in Mozambique. malaria climate precipitation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Malaria remains a major global public health issue with over 260 million cases and over 500,000 deaths [ 1 ]. While this represents a decline in malaria deaths, there has been an increase in cases globally [ 1 ]. There have been calls to action to address the highest burden areas and inequities in malaria control, which may be responsible to the decreasing case fatality [ 1 , 2 ]. However, the increase in cases is still troubling. There are multiple malaria prevention interventions available including, but not limited to: insecticide treated bednets; indoor residual spraying; intermittent preventative treatment during pregnancy; seasonal malaria chemoprevention; integrated community case management; and vaccination [ 1 – 3 ]. These tools typically are combined to form intervention packages by malaria control programs in endemic countries. There have been significant challenges for malaria control programs in recent years [ 1 , 2 ]. The COVID-19 pandemic made access to healthcare and interventions difficult in some environments and impossible in others [ 4 ]. The other major challenge is the impact that climate change has had on malaria transmission and risk [ 5 – 12 ]. Climate change is impacting the seasonal weather trends [ 13 – 16 ]. In many malaria endemic areas this impacts the seasonal rain and temperature patterns, which also are associated with mosquito proliferation and risk of infection [ 17 – 19 ]. In addition to these seasonal changes, there has been an increase in the frequency and intensity of severe weather events [ 13 , 20 – 26 ]. While severe weather events have been increasing they are still rare events epidemiologically. This makes it difficult to investigate their association with malaria risk directly. Additionally, not all severe weather events reach the definitions to be documented and named a cyclone or tropical storm. Mozambique is one country that has been experiencing significant impacts of climate change [ 23 , 24 , 27 , 28 ]. These impacts include changing weather patterns and more frequent and intense storms [ 20 , 24 , 27 , 28 ]. Mozambique also has the 5th highest malaria prevalence globally [ 1 ]. Current malaria prevention interventions throughout the include ITN distributions though antenatal care centers and iCCM [ 1 ]. In the southern region of the country (Maputo, Inhambane, and Gaza Provinces) where the prevalence and incidence are lowest, there is a focus on local malaria elimination [ 29 ]. Increased interventions used in this area include mass ITN distributions and IRS. In the northern region of the country (Zambezia, Nampula, and Cabo Delgado) where the prevalence and incidence are highest, there is a focus on gaining control over transmission and preventing morbidity and mortality [ 29 ]. Interventions in this area include mass ITN distributions, school-based ITN distributions, and IRS, as well as introduction of the RTSS vaccine [ 29 ]. Over the past 35 years, Mozambique has experienced over 75 declared natural disasters related to floods, droughts, and cyclones [ 30 ]. During 2005–2024 Mozambique had 24 named tropical cyclones and storms that severely impacted the population, which is a marked increase in frequency and population impacted [ 30 ]. The most notable of these was Cyclone Idai in 2019, which was one of the worst tropical cyclones recorded in the Southern Hemisphere [ 31 ]. The storm caused catastrophic damage and led to a humanitarian crisis in Mozambique, as well as Zimbabwe and Malawi [ 31 , 32 ]. In the aftermath, the affected population suffered a cholera epidemic on top of the housing and infrastructure destruction and loss of life [ 33 , 34 ]. Most recently, Cyclone Freddy in 2023 had substantial impacts on the entire country having made landfall multiple times and lasting over 3-weeks in duration, and Cyclone Chido in 2024 making landfall in Cabo Delgado Province causing devastating impacts to Mecufi District where 100% of households were estimated to be damaged or destroyed [ 27 , 35 , 36 ]. There have also been recorded increases in other severe storms that do not meet the criteria for tropical cyclones and storms [ 30 ]. These storms have all impacted the efficiency of the Mozambique National Malaria Control Program to achieve their goals [ 20 , 37 , 38 ]. In addition to these named cyclones and tropical storms there have been increases of other severe storms and irregular rainfall patterns [ 24 , 27 , 39 ]. It is imperative to understand when and how to best respond the impacts of these events. Additionally, it is important to track these events to prepare in advance with additional prevention supplies (e.g. ITNs, IRS materials, testing and treatment materials, etc.). There have been multiple studies that have investigated the temporal lag in precipitation and malaria risk in many locations. Most of these cover large geographic regions [ 40 – 44 ]. Currently, these studies show temporal lags around 8–10 weeks, however there is geographic variability [ 40 – 44 ]. The goal of our study was to determine the appropriate temporal lag between increased precipitation and malaria risk in Mozambique overall. We also aimed to investigate this by region of the country. Methods Malaria case data Malaria case count data were provided by the Mozambique National Malaria Control Program (NMCP). These are surveillance data of malaria cases that present at health facilities (hospitals, health centers, and health posts) or to community health workers. These include both uncomplicated and severe malaria cases (hospitalizations) diagnosed by rapid diagnostic test or microscopy. The dataset also included malaria deaths. These are collected at each health facility and aggregated by month and transmitted to the District Health Office, these are then aggregated for the district and transmitted to the Provincial Health Office, then to the NMCP. We had access to monthly level malaria case counts per District from 2017 through 2022. These data also contained district level population counts per month to calculate malaria prevalence and incidence. This study was deemed not human subjects research by the University of Minnesota Institutional Review Board. Precipitation data Precipitation data were obtained from NASA GIS DISC Earth Data. These were collected as gridded raster data at a spatial resolution of 0.1 x 0.1 degree and at a daily temporal resolution. We obtained data from 2016 through 2022 to allow for analysis of temporal lags in the association with malaria cases. We aggregated these to monthly mean and total precipitation to the district level using a spatial join. This resulted in monthly mean and total precipitation per district from 2016 through 2022 throughout Mozambique. Exploratory Analyses We mapped out the population distribution by year and district to determine if there were any temporal trends or changes in population over the analysis period. We then mapped out the malaria prevalence per month by district to determine the spatial and temporal distribution. This also was done to evaluate potential impacts of the COVID-19 pandemic on malaria case reporting and healthcare utilization in 2019-2020. We repeated these analyses for malaria hospitalizations and deaths. We investigated the correlation between precipitation and malaria-related values at the district level each month with the spatial and temporal correlations considered. We used Pearson correlation coefficients to quantify the correlations. The correlations for each district were calculated between the month’s precipitation values and the corresponding temporal lagged malaria cases, malaria hospitalizations and malaria deaths. The goal of this analysis was to determine if there was an association between precipitation and malaria outcomes and the appropriate temporal lag for the association between precipitation and malaria risk. As there were lower numbers of malaria hospitalizations and deaths, we focused on malaria cases for the remainder of the analysis. We graphed the relationship between precipitation and malaria cases over time to estimate appropriate temporal lags for the entirety of Mozambique. We also investigated the temporally lagged effects of precipitation on malaria cases with temporal lags of 0-, 1-, and 2-months. Statistical and Stratified Analyses We recognized spatial heterogeneity in the correlations between precipitation and malaria cases across Mozambique. We stratified the country into three regions (North, Central, South). To conduct a more granular analysis, we used weekly malaria case data, which excludes case data from community health workers, to investigate the association between temporal lags in precipitation and malaria risk. We used Poisson generalized linear mixed models (GLMs) to determine the relative risk of malaria associated with precipitation for each of the three regions in Mozambique. The GLM used 24 week’s precipitation values as the predictors to model the malaria cases, with the mean of all the precipitation as the reference value when calculating the relative risk. Specifically, let denote the weekly malaria case values for the th district, let denote the th lagged weekly precipitation values for the th district. We selected all districts belonging to the required regions (North, etc) and fit the following Poisson GLM: where denotes the expectation of weekly malaria case values, and denotes the estimated coefficient for the th lagged weekly precipitation values from the model. The relative risk (RR) values for the th lagged week of the precipitation at the value of are then predicted as , where is the reference value and we used the mean of all the precipitation data for the required regions. Results We found evidence of strong spatial variation in malaria cases, malaria hospitalizations, and malaria deaths (Fig. 1 ). Malaria hospitalizations and deaths were more concentrated in northern districts, while malaria cases were distributed throughout the country (Fig. 1 ). Examining the cross-correlation between monthly precipitation and malaria cases across a subset of districts showed high levels of correlation at both 1-month and 2-month lags (Fig. 2 ). The spatial distribution of the correlation coefficients for malaria cases and malaria hospitalizations showed increased correlation across Mozambique for 1-month and 2-month lags (Fig. 3 ). The spatial distribution of the correlation coefficients for malaria deaths did not show substantial spatial heterogeneity and this did not change with different temporal lags (Fig. 3 ). When examining the time-series plots of precipitation and malaria cases (Fig. 4 ), there was evidence of a clear temporal relationship throughout Mozambique. Heat maps of the association between precipitation and the relative risk of malaria prevalence were created for all of Mozambique, and stratified by region (North, Central, and South) (Fig. 5 ). For the entire country, there was a clear increased relative risk for malaria at an 8-week temporal lag, which matches with the overall 2-month lag seen previously. However, this varied when stratified by region. In the Northern Region there was an increased relative risk for malaria between 6-week and 8-week temporal lags. In the Central region there was an increased relative risk for malaria between 10-week and 12-week temporal lags. In the Southern region there was an increased relative risk for malaria between 10-week and 13-week temporal lags. Discussion In our study we found strong evidence of spatial and temporal correlation between precipitation and malaria risk. When we investigated the association between precipitation and malaria risk at the national level, our results were consistent with the current literature. These results indicated an approximate 8-week lag between elevated precipitation and increased malaria risk. However, when we stratified this by region of Mozambique, we found heterogeneity in these lag periods. We found that in the Northern region of Mozambique, this lag is slightly shorter, between 6–8 weeks. In the Central region of Mozambique we found the lag to be between 8–10 weeks. And in the Northern region of Mozambique the lag was 10–13 weeks. While all these values fall within ranges previously described in other locations, they represent important differences for both further modeling and decision making in Mozambique. In Mozambique at the national level Armando et al. used a 1–2 month lag for precipitation, which is consistent with the 8-week lag that we found nationally [ 40 , 42 ]. In a study in neighboring Zimbabwe by Gunda et al. a 4-week lag was used for the study area of interest [ 41 ]. In Ghana Krefis et al. found a 9-week lag to be most appropriate for their study area [ 43 ]. These findings, along with others show that there is heterogeneity between countries and study areas in the appropriate lag time in the association between precipitation events and malaria risk. Our study bolsters the current literature by investigating the specifics of the temporal lag in the association between precipitation and malaria risk in Mozambique. We have shown that at the national aggregate the lag time is consistent with other studies, while disaggregated to regions the lag times vary. These findings are important for informing modeling of climate and environment and malaria risk. Many studies use only aggregate data to determine lag times for models when there is geographic variability to also account for. Additionally, these findings are important programmatically for planning and responding to elevated precipitation events and severe storms. Knowledge of the specific lag times for different regions of Mozambique can be used to respond in a timely manner to severe storms, particularly in planning when to deliver interventions after a storm to have the maximum impact. Conclusion With increasing storms and changing weather patterns due to climate change there is a need to understand the specifics of the association between elevated precipitation and malaria risk. We found important spatial heterogeneity in temporal lag times between precipitation events and malaria risk in Mozambique. These findings provide important information for further modeling and planning of the timing of interventions in response to storms and seasonal precipitation. Declarations Competing Interests The authors declare that they have no competing interests. Funding This research was funded by the Wellcome Trust program “Digital Tools to Transform Infectious Disease Modelling” [226053/Z/22/Z] Author Contribution HZ conducted the analyses and wrote the first draft of the manuscript. LZ assisted in the design and supervised the analysis. JS supervised the database creation and merging of datasets. MC compiled the database. BC oversaw the malaria surveillance program. MS compiled and organized the malaria surveillance data. KMS designed the study, oversaw the analysis, and finalized the manuscript. Acknowledgements Our team would like extend our gratitude to the large network on malaria scientists who gathered and assembled surveillance data at the local, district, provincial and national levels in Mozambique. We also would like to thank colleagues at Navitas Group Global in Mozambique who assisted in the data transfer arrangements. Data Availability The data used for this study were provided by the National Malaria Control Program in Mozambique. Requests for those data should be made to the Mozambique Ministry of Health. References WHO. World Malaria Report 2024. World Health Organization: Geneva, Switzerland; 2024. WHO. World Malaria Report 2023. Switzerland: Geneva; 2023. WHO. WHO recommends R21/Matrix-M vaccine for malaria prevention in updated advice on immunization . 2023. Formenti B, et al. The impact of COVID-19 on communicable and non-communicable diseases in Africa: a narrative review. Infez Med. 2022;30(1):30–40. Abiodun GJ, et al. Exploring the Impact of Climate Variability on Malaria Transmission Using a Dynamic Mosquito-Human Malaria Model. Open Infect Dis J. 2018;10:88–100. Alimi TO, et al. Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population. Parasit Vectors. 2015;8:431. Arndt C, et al. Adapting to climate change: an integrated biophysical and economic assessment for Mozambique. Sustain Sci. 2011;6(1):7–20. Babaie J, et al. A systematic evidence review of the effect of climate change on malaria in Iran. J Parasit Dis. 2018;42(3):331–40. Bouma MJ, et al. Global malaria maps and climate change: a focus on East African highlands. Trends Parasitol. 2011;27(10):421–2. Caminade C, et al. Impact of climate change on global malaria distribution. Proc Natl Acad Sci U S A. 2014;111(9):3286–91. Dasgupta S. Burden of climate change on malaria mortality. Int J Hyg Environ Health. 2018;221(5):782–91. Hales S, Woodward A. Climate change will increase demands on malaria control in Africa. Lancet. 2003;362(9398):1775. Romanello M, et al. The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action. Lancet. 2024;404(10465):1847–96. Romanello M, et al. The 2023 report of the Lancet Countdown on health and climate change: the imperative for a health-centred response in a world facing irreversible harms. Lancet. 2023;402(10419):2346–94. Agyekum TP et al. A Systematic Review of the Effects of Temperature on Anopheles Mosquito Development and Survival: Implications for Malaria Control in a Future Warmer Climate. Int J Environ Res Public Health, 2021. 18(14). Nissan H, Ukawuba I, Thomson M. Climate-proofing a malaria eradication strategy. Malar J. 2021;20(1):190. Marques R, et al. Climate change impacts on Anopheles (K.) cruzii in urban areas of Atlantic Forest of Brazil: Challenges for malaria diseases. Acta Trop. 2021;224:106123. Chaturvedi S, Dwivedi S. Understanding the effect of climate change in the distribution and intensity of malaria transmission over India using a dynamical malaria model. Int J Biometeorol. 2021;65(7):1161–75. Mihailovic DT, et al. Assessment of climate change impact on the malaria vector Anopheles hyrcanus, West Nile disease, and incidence of melanoma in the Vojvodina Province (Serbia) using data from a regional climate model. PLoS ONE. 2020;15(1):e0227679. Searle KM, et al. Household storm damage limits use of and access to insecticide treated bednets in Mozambique. Ann Hum Biol. 2025;52(1):2461145. Liu M et al. Response of Extreme Rainfall for Landfalling Tropical Cyclones Undergoing Extratropical Transition to Projected Climate Change: Hurricane Irene (2011). Earths Future, 2020. 8(3): p. e2019EF001360. Chen J et al. Impacts of climate change on tropical cyclones and induced storm surges in the Pearl River Delta region using pseudo-global-warming method. Sci Rep, 2020. 10(1): p. 1965. Varo R, Rodo X, Bassat Q. Climate change, cyclones and cholera - Implications for travel medicine and infectious diseases. Travel Med Infect Dis. 2019;29:6–7. Hope M. Cyclones in Mozambique may reveal humanitarian challenges of responding to a new climate reality. Lancet Planet Health. 2019;3(8):e338–9. Sriver RL. Climate change: Man-made cyclones. Nature. 2011;479(7371):50–1. Sriver RL. Climate change: Tropical cyclones in the mix. Nature. 2010;463(7284):1032–3. Focus Adriano L, Nazir A, Uwishema O. The devastating effect of cyclone Freddy amidst the deadliest cholera outbreak in Malawi: a double burden for an already weak healthcare system-short communication. Ann Med Surg (Lond). 2023;85(7):3761–3. Cambaza E et al. Outbreak of Cholera Due to Cyclone Kenneth in Northern Mozambique, 2019. Int J Environ Res Public Health, 2019. 16(16). PNCM, Strategic Malaria Plan 2017–2022 . 2017, Republic of Mozambique Ministry of Health: Maputo, Mozambique. USAID, Disaster Response - Mozambique . 2021. Warren M. Why Cyclone Idai is one of the Southern Hemisphere's most devastating storms. Nature, 2019. Chen WH, Azman AS. Mitigating Cholera in the Aftermath of Cyclone Idai. Am J Trop Med Hyg. 2019;101(5):960–2. Lequechane JD, et al. Mozambique's response to cyclone Idai: how collaboration and surveillance with water, sanitation and hygiene (WASH) interventions were used to control a cholera epidemic. Infect Dis Poverty. 2020;9(1):68. Devi S. Cyclone Idai: 1 month later, devastation persists. Lancet. 2019;393(10181):1585. Twabi HH, et al. Cyclone Freddy and its impact on maternal health service utilisation: Cross-sectional analysis of data from a national maternal surveillance platform in Malawi. PLOS Glob Public Health. 2024;4(8):e0003565. Cursino M. Cyclone Chido kills 94 people in Mozambique , in BBC News . 2024. Glowac C, Ferrao JL, Searle KM. The association between infrastructure damage in the aftermath of Cyclone Idai and malaria risk in Sofala Province, Mozambique: an ecological study. Malar J. 2024;23(1):355. Searle KM, et al. Long-lasting household damage from Cyclone Idai increases malaria risk in rural western Mozambique. Sci Rep. 2023;13(1):21590. Tompkins AM, Caporaso L. Assessment of malaria transmission changes in Africa, due to the climate impact of land use change using Coupled Model Intercomparison Project Phase 5 earth system models. Geospat Health. 2016;11(1 Suppl):380. Armando CJ, et al. Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018. Sci Rep. 2025;15(1):11971. Gunda R, et al. Malaria incidence trends and their association with climatic variables in rural Gwanda, Zimbabwe, 2005–2015. Malar J. 2017;16(1):393. Armando CJ, et al. Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016–2018: a spatial temporal analysis. Front Public Health. 2023;11:1162535. Krefis AC, et al. Modeling the relationship between precipitation and malaria incidence in children from a holoendemic area in Ghana. Am J Trop Med Hyg. 2011;84(2):285–91. Liu Q, et al. Association of temperature and precipitation with malaria incidence in 57 countries and territories from 2000 to 2019: A worldwide observational study. J Glob Health. 2024;14:04021. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Mar, 2026 Reviews received at journal 15 Mar, 2026 Reviewers agreed at journal 01 Mar, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 21 Nov, 2025 Reviews received at journal 08 Nov, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers invited by journal 13 Oct, 2025 Editor assigned by journal 05 Oct, 2025 Submission checks completed at journal 05 Oct, 2025 First submitted to journal 30 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7755148","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534182565,"identity":"581a47bb-6d5e-4797-a70d-7e60e12d4962","order_by":0,"name":"Hengcheng Zhu","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Hengcheng","middleName":"","lastName":"Zhu","suffix":""},{"id":534182567,"identity":"100a5669-cdc7-4ce6-a329-179bc71cba55","order_by":1,"name":"Lin Zhang","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhang","suffix":""},{"id":534182568,"identity":"8afac801-a069-4d2a-a8e5-9078000c1ce6","order_by":2,"name":"Euna Khan","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Euna","middleName":"","lastName":"Khan","suffix":""},{"id":534182569,"identity":"0dc6d507-2f4d-42ba-bc48-3f245dbae691","order_by":3,"name":"Jaideep Srivastava","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Jaideep","middleName":"","lastName":"Srivastava","suffix":""},{"id":534182572,"identity":"337587bd-9fa5-465d-9942-33437a10a912","order_by":4,"name":"Mitchel Croal","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Mitchel","middleName":"","lastName":"Croal","suffix":""},{"id":534182573,"identity":"f9c4e9d5-bc02-4242-b248-31133e0d6cc8","order_by":5,"name":"Baltazar Candrinho","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Baltazar","middleName":"","lastName":"Candrinho","suffix":""},{"id":534182574,"identity":"8534cf7d-9fca-48b5-8fa3-de1043b441d7","order_by":6,"name":"Mariana Silva","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Mariana","middleName":"","lastName":"Silva","suffix":""},{"id":534182575,"identity":"5524ba13-cb6e-4239-b834-28e6dc468302","order_by":7,"name":"Kelly M. Searle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACCTBZwCAH4bIRrcWAwZh0LYkNRGuR7D988HOFgU36hmtnDzB8KDtMWIu0RFqy5BmDtNwNt/MSGGecI0KLnASPgWSDwWGglhwDZt42YrTwn//8s8Hgf7oBSMtfYrRIM+SwAW05kADWwkiMFskZaWaWDQbJhjOBWg72nEsnrEXi/OHHNxsq7OT5bucYPvhRZk1YCwo4QKL6UTAKRsEoGAW4AABmejiT/5VzHgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Minnesota","correspondingAuthor":true,"prefix":"","firstName":"Kelly","middleName":"M.","lastName":"Searle","suffix":""}],"badges":[],"createdAt":"2025-09-30 22:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7755148/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7755148/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94480942,"identity":"72caa415-5eba-4d92-aadb-cf7b66410e51","added_by":"auto","created_at":"2025-10-27 16:12:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":527518,"visible":true,"origin":"","legend":"","description":"","filename":"EDAmanuscriptHengcheng9.25.2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/87b1245ed4352d7fa7afee17.docx"},{"id":94481073,"identity":"eea51b2d-1357-47ea-87a6-701dce6de7a0","added_by":"auto","created_at":"2025-10-27 16:12:33","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9994,"visible":true,"origin":"","legend":"","description":"","filename":"a18f026094094ec4bf8e5f6b97772aea.json","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/8f9f6b3ed214094f3d5d0ab6.json"},{"id":94489770,"identity":"19558a26-cdaa-4d59-819a-9cfe3c319c71","added_by":"auto","created_at":"2025-10-27 17:05:55","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76671,"visible":true,"origin":"","legend":"","description":"","filename":"a18f026094094ec4bf8e5f6b97772aea1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/2af7675fd11ceca5cc4b90d7.xml"},{"id":94481621,"identity":"299074e3-5def-44a4-b22e-6b6b8449319b","added_by":"auto","created_at":"2025-10-27 16:13:56","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/acb90dab906020d557cfa910.png"},{"id":94480621,"identity":"45801864-ab44-4079-8b8c-7b7491649369","added_by":"auto","created_at":"2025-10-27 16:11:27","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/8aa4d7e8336f06498c1ae4ca.png"},{"id":94481138,"identity":"635b0d72-b1eb-489a-944d-4cc5a24f719a","added_by":"auto","created_at":"2025-10-27 16:12:46","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/10f0dd00ea8d690a7f42ffc1.png"},{"id":94481421,"identity":"00d79146-5090-4a9f-b8ea-162ffa2f0a2f","added_by":"auto","created_at":"2025-10-27 16:13:20","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/636335a9b927bd2463d376e4.jpeg"},{"id":94481445,"identity":"c66401ee-504e-42c1-9ffe-56f85ed295b9","added_by":"auto","created_at":"2025-10-27 16:13:23","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":441604,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/9e8a5048938654bad7645745.jpeg"},{"id":94481217,"identity":"5343ae3e-ccce-4cdc-a650-1adaff15af89","added_by":"auto","created_at":"2025-10-27 16:12:53","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/cc099884d40e87107e4d3201.png"},{"id":94481469,"identity":"40bfb49d-ab8d-435b-a982-c6caa7cb7c5a","added_by":"auto","created_at":"2025-10-27 16:13:28","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/955bae86004325cc865f9beb.jpeg"},{"id":94481024,"identity":"48f0eac8-7993-4c1e-9a1d-c04794adade7","added_by":"auto","created_at":"2025-10-27 16:12:25","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":321,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/e684a15bca7d46a1aef6f7f5.png"},{"id":94481131,"identity":"10139f5a-ba7d-4c2a-96f0-367041776fa6","added_by":"auto","created_at":"2025-10-27 16:12:45","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":321,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/f80550cc752fc2804c28716c.png"},{"id":94480816,"identity":"17464838-c73f-4e2e-9f6a-13ff17b25985","added_by":"auto","created_at":"2025-10-27 16:12:01","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":321,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/c29171859c226c546c2d1d94.png"},{"id":94480418,"identity":"7445c24e-e7ea-405e-ae64-6c26b9560874","added_by":"auto","created_at":"2025-10-27 16:11:03","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/e0aa2ef3cdd0046c6ee74bc6.png"},{"id":94481120,"identity":"8eda787a-9920-4cbd-aae8-170fdcef5cf2","added_by":"auto","created_at":"2025-10-27 16:12:43","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82823,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/f50bb6a75f75d1e2d6e96a06.png"},{"id":94481410,"identity":"efa82f6f-4a18-4cc9-b232-8a2d3667b7a3","added_by":"auto","created_at":"2025-10-27 16:13:18","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":321,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/abce96d523d00286a995e592.png"},{"id":94481011,"identity":"00e4e2d8-0fe9-4089-86a8-b9b0b315e9f8","added_by":"auto","created_at":"2025-10-27 16:12:23","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/bf9f52ff047f9372e8d84cdd.png"},{"id":94481173,"identity":"e4ca6da5-10d7-4d01-bf7b-7bd950db5f02","added_by":"auto","created_at":"2025-10-27 16:12:51","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75454,"visible":true,"origin":"","legend":"","description":"","filename":"a18f026094094ec4bf8e5f6b97772aea1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/d97053f4d1196700f5f0e09e.xml"},{"id":94481636,"identity":"c0532508-261c-4dbe-844f-b0189271c886","added_by":"auto","created_at":"2025-10-27 16:14:00","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84753,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/4dfac9f3db58651cb84e9377.html"},{"id":94481174,"identity":"930c7662-33e0-4bb6-8ec7-548af7767cca","added_by":"auto","created_at":"2025-10-27 16:12:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":352563,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of A) malaria cases, B) malaria hospitalizations, and C) malaria deaths by year (2017-2022)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/ed172be3ca411e1f0ff16a63.png"},{"id":94480785,"identity":"f2917cf4-2cf2-4aa6-9e56-522c9d15fdd3","added_by":"auto","created_at":"2025-10-27 16:11:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60712,"visible":true,"origin":"","legend":"\u003cp\u003eCross-correlation of malaria cases and precipitation by monthly lag for a subset of two districts in Mozambique\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/0b3e81ff6f716021dd32955f.png"},{"id":94481558,"identity":"c46667a7-957e-487b-b005-448f01e274c8","added_by":"auto","created_at":"2025-10-27 16:13:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":335146,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the correlation coefficient between precipitation and A) malaria cases, B) malaria hospitalizations, and C) malaria deaths by temporal lags of lag0 (no lag), lag1 (1 month), and lag2 (2 months)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/0e39cc9b9f51d9a817ecda76.png"},{"id":94481564,"identity":"da293b22-2330-46b8-b750-cfbbfc1f4baf","added_by":"auto","created_at":"2025-10-27 16:13:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":115683,"visible":true,"origin":"","legend":"\u003cp\u003eTime series between monthly malaria cases and monthly total precipitation at the district level\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/a227796278d7b5617a3a4f2a.png"},{"id":94480959,"identity":"73a731a6-8b5e-439a-ab10-6bfd9b463124","added_by":"auto","created_at":"2025-10-27 16:12:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":254325,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmaps of the relative risk for malaria by weekly time lags\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/871e4554d08084da20e7ae2f.png"},{"id":94491272,"identity":"a318834a-8a9f-4043-bffb-9cac2b33dd5f","added_by":"auto","created_at":"2025-10-27 17:24:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1403467,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7755148/v1/b64b3ce0-2cc9-497e-89aa-2e7d0397b87c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploratory analysis of the correlation between precipitation and storms and malaria prevalence at the national scale in Mozambique","fulltext":[{"header":"Background","content":"\u003cp\u003eMalaria remains a major global public health issue with over 260\u0026nbsp;million cases and over 500,000 deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While this represents a decline in malaria deaths, there has been an increase in cases globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. There have been calls to action to address the highest burden areas and inequities in malaria control, which may be responsible to the decreasing case fatality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the increase in cases is still troubling. There are multiple malaria prevention interventions available including, but not limited to: insecticide treated bednets; indoor residual spraying; intermittent preventative treatment during pregnancy; seasonal malaria chemoprevention; integrated community case management; and vaccination [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese tools typically are combined to form intervention packages by malaria control programs in endemic countries. There have been significant challenges for malaria control programs in recent years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The COVID-19 pandemic made access to healthcare and interventions difficult in some environments and impossible in others [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The other major challenge is the impact that climate change has had on malaria transmission and risk [\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eClimate change is impacting the seasonal weather trends [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In many malaria endemic areas this impacts the seasonal rain and temperature patterns, which also are associated with mosquito proliferation and risk of infection [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition to these seasonal changes, there has been an increase in the frequency and intensity of severe weather events [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24 CR25\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. While severe weather events have been increasing they are still rare events epidemiologically. This makes it difficult to investigate their association with malaria risk directly. Additionally, not all severe weather events reach the definitions to be documented and named a cyclone or tropical storm.\u003c/p\u003e\u003cp\u003eMozambique is one country that has been experiencing significant impacts of climate change [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These impacts include changing weather patterns and more frequent and intense storms [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Mozambique also has the 5th highest malaria prevalence globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Current malaria prevention interventions throughout the include ITN distributions though antenatal care centers and iCCM [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the southern region of the country (Maputo, Inhambane, and Gaza Provinces) where the prevalence and incidence are lowest, there is a focus on local malaria elimination [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Increased interventions used in this area include mass ITN distributions and IRS. In the northern region of the country (Zambezia, Nampula, and Cabo Delgado) where the prevalence and incidence are highest, there is a focus on gaining control over transmission and preventing morbidity and mortality [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Interventions in this area include mass ITN distributions, school-based ITN distributions, and IRS, as well as introduction of the RTSS vaccine [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOver the past 35 years, Mozambique has experienced over 75 declared natural disasters related to floods, droughts, and cyclones [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. During 2005\u0026ndash;2024 Mozambique had 24 named tropical cyclones and storms that severely impacted the population, which is a marked increase in frequency and population impacted [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The most notable of these was Cyclone Idai in 2019, which was one of the worst tropical cyclones recorded in the Southern Hemisphere [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The storm caused catastrophic damage and led to a humanitarian crisis in Mozambique, as well as Zimbabwe and Malawi [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In the aftermath, the affected population suffered a cholera epidemic on top of the housing and infrastructure destruction and loss of life [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Most recently, Cyclone Freddy in 2023 had substantial impacts on the entire country having made landfall multiple times and lasting over 3-weeks in duration, and Cyclone Chido in 2024 making landfall in Cabo Delgado Province causing devastating impacts to Mecufi District where 100% of households were estimated to be damaged or destroyed [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. There have also been recorded increases in other severe storms that do not meet the criteria for tropical cyclones and storms [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese storms have all impacted the efficiency of the Mozambique National Malaria Control Program to achieve their goals [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In addition to these named cyclones and tropical storms there have been increases of other severe storms and irregular rainfall patterns [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. It is imperative to understand when and how to best respond the impacts of these events. Additionally, it is important to track these events to prepare in advance with additional prevention supplies (e.g. ITNs, IRS materials, testing and treatment materials, etc.).\u003c/p\u003e\u003cp\u003eThere have been multiple studies that have investigated the temporal lag in precipitation and malaria risk in many locations. Most of these cover large geographic regions [\u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Currently, these studies show temporal lags around 8\u0026ndash;10 weeks, however there is geographic variability [\u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The goal of our study was to determine the appropriate temporal lag between increased precipitation and malaria risk in Mozambique overall. We also aimed to investigate this by region of the country.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eMalaria case data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMalaria case count data were provided by the Mozambique National Malaria Control Program (NMCP). These are surveillance data of malaria cases that present at health facilities (hospitals, health centers, and health posts) or to community health workers. These include both uncomplicated and severe malaria cases (hospitalizations) diagnosed by rapid diagnostic test or microscopy. The dataset also included malaria deaths. These are collected at each health facility and aggregated by month and transmitted to the District Health Office, these are then aggregated for the district and transmitted to the Provincial Health Office, then to the NMCP. We had access to monthly level malaria case counts per District from 2017 through 2022. These data also contained district level population counts per month to calculate malaria prevalence and incidence. This study was deemed not human subjects research by the University of Minnesota Institutional Review Board.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrecipitation data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePrecipitation data were obtained from NASA GIS DISC Earth Data. These were collected as gridded raster data at a spatial resolution of 0.1 x 0.1 degree and at a daily temporal resolution. We obtained data from 2016 through 2022 to allow for analysis of temporal lags in the association with malaria cases. We aggregated these to monthly mean and total precipitation to the district level using a spatial join. This resulted in monthly mean and total precipitation per district from 2016 through 2022 throughout Mozambique.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExploratory Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe mapped out the population distribution by year and district to determine if there were any temporal trends or changes in population over the analysis period. We then mapped out the malaria prevalence per month by district to determine the spatial and temporal distribution. This also was done to evaluate potential impacts of the COVID-19 pandemic on malaria case reporting and healthcare utilization in 2019-2020. We repeated these analyses for malaria hospitalizations and deaths.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe investigated the correlation between precipitation and malaria-related values at the district level each month with the spatial and temporal correlations considered. We used Pearson correlation coefficients to quantify the correlations. The correlations for each district were calculated between the month\u0026rsquo;s precipitation values and the corresponding temporal lagged malaria cases, malaria hospitalizations and malaria deaths. The goal of this analysis was to determine if there was an association between precipitation and malaria outcomes and the appropriate temporal lag for the association between precipitation and malaria risk. As there were lower numbers of malaria hospitalizations and deaths, we focused on malaria cases for the remainder of the analysis. We graphed the relationship between precipitation and malaria cases over time to estimate appropriate temporal lags for the entirety of Mozambique. We also investigated the temporally lagged effects of precipitation on malaria cases with temporal lags of 0-, 1-, and 2-months.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical and Stratified Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe recognized spatial heterogeneity in the correlations between precipitation and malaria cases across Mozambique. We stratified the country into three regions (North, Central, South). To conduct a more granular analysis, we used weekly malaria case data, which excludes case data from community health workers, to investigate the association between temporal lags in precipitation and malaria risk. We used Poisson generalized linear mixed models (GLMs) to determine the relative risk of malaria associated with precipitation for each of the three regions in Mozambique. The GLM used 24 week\u0026rsquo;s precipitation values as the predictors to model the malaria cases, with the mean of all the precipitation as the reference value when calculating the relative risk. Specifically, let \u003cimg width=\"12\" height=\"19\" src=\"data:image/png;base64,R0lGODlhEgAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAEABwAPABUAhAAAAAAAAAAAOgAAZgA6ZgA6kABmtjoAADqQ22YAAGa222a2/5A6AJCQZpC225Db/7ZmALZmOrb/27b//9uQOtuQZtv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwVuIBSMhQUA2DEKD6AxATGdZxUIDn0FJQ1QLF8mMZidUgvfSZQEDBHKEyUABUAM0eOhROllX4JGMUubBsnSAFYJGS+pypcX3EKjDm70zpsONF8xRmVjQyMBeVZ8ZE92Wk12QHV2bRIRj1kXBwIKACEAOw==\" alt=\"image\"\u003e\u0026nbsp;denote the weekly malaria case values for the \u003cimg width=\"5\" height=\"19\" src=\"data:image/png;base64,R0lGODlhCAAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAEACAAGAA8AhAAAAAAAAAAAOgAAZgA6kABmtjoAADqQ22YAAGa222a2/5A6AJDb/7ZmALaQOrb//9v///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwUuICA2wSFSi8CIbMtOSFCKsEojwyNKBgGJkdkooDgtcrveDxAssJoUxwEVECQgIQA7\" alt=\"image\"\u003eth district, let \u003cimg width=\"47\" height=\"21\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;denote the \u003cimg width=\"6\" height=\"19\" src=\"data:image/png;base64,R0lGODlhCQAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACAAIABQAhAAAAAAAAAAAOgAAZgA6kABmtjoAADqQ22YAAGaQtma2/5A6AJDb/7ZmALb//9uQOtu2kNv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwU7ICCKUlCMYhMoaOuO1RKYaLzWi8CgFDI4qInhhHoEDi0VC7b4BQ2ECKpEHKmQTN0zWjyiIFDpSJAQj0IAOw==\" alt=\"image\"\u003eth lagged weekly precipitation values for the \u003cimg width=\"5\" height=\"19\" src=\"data:image/png;base64,R0lGODlhCAAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAEACAAGAA8AhAAAAAAAAAAAOgAAZgA6kABmtjoAADqQ22YAAGa222a2/5A6AJDb/7ZmALaQOrb//9v///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwUuICA2wSFSi8CIbMtOSFCKsEojwyNKBgGJkdkooDgtcrveDxAssJoUxwEVECQgIQA7\" alt=\"image\"\u003eth district. We selected all districts belonging to the required regions (North, etc) and fit the following Poisson GLM:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u003cimg width=\"345\" height=\"21\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cimg width=\"34\" height=\"19\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;denotes the expectation of weekly malaria case values, and \u0026nbsp;\u003cimg width=\"15\" height=\"21\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;denotes the estimated coefficient for the \u003cimg width=\"6\" height=\"19\" src=\"data:image/png;base64,R0lGODlhCQAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACAAIABQAhAAAAAAAAAAAOgAAZgA6kABmtjoAADqQ22YAAGaQtma2/5A6AJDb/7ZmALb//9uQOtu2kNv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwU7ICCKUlCMYhMoaOuO1RKYaLzWi8CgFDI4qInhhHoEDi0VC7b4BQ2ECKpEHKmQTN0zWjyiIFDpSJAQj0IAOw==\" alt=\"image\"\u003eth lagged weekly precipitation values from the model.\u003c/p\u003e\n\u003cp\u003eThe relative risk (RR) values for the\u0026nbsp;\u003cimg width=\"6\" height=\"19\" src=\"data:image/png;base64,R0lGODlhCQAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACAAIABQAhAAAAAAAAAAAOgAAZgA6kABmtjoAADqQ22YAAGaQtma2/5A6AJDb/7ZmALb//9uQOtu2kNv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwU7ICCKUlCMYhMoaOuO1RKYaLzWi8CgFDI4qInhhHoEDi0VC7b4BQ2ECKpEHKmQTN0zWjyiIFDpSJAQj0IAOw==\" alt=\"image\"\u003eth lagged week of the precipitation at the value of\u0026nbsp;\u003cimg width=\"11\" height=\"19\" src=\"data:image/png;base64,R0lGODlhEAAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABwAQABAAhAAAAAAAAAAAOgAAZgA6ZgA6kABmtjoAADo6ADqQ22YAAGY6AGZmAGa222a2/5A6AJDb/7ZmALZmOrbb/7b//9uQOtv/ttv///+2Zv+2kP/bkP//tv//2wECAwECAwECAwVhIABwUmAOFKAdpgCJYhQME4wFQgOLd3HBlYBjJ9ooULxAgggUqg4GJmxVsCiiUhHnIUD4sk0kePSYpcaRAcMJrrhuWOltaBQTV3HZkGj8Jv1aZXtJLjsLJgQ1ABksOC8iIQA7\" alt=\"image\"\u003e\u0026nbsp;are then predicted as\u0026nbsp;\u003cimg width=\"126\" height=\"21\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e, where\u0026nbsp;\u003cimg width=\"29\" height=\"21\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;is the reference value and we used the mean of all the precipitation data for the required regions. \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe found evidence of strong spatial variation in malaria cases, malaria hospitalizations, and malaria deaths (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Malaria hospitalizations and deaths were more concentrated in northern districts, while malaria cases were distributed throughout the country (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eExamining the cross-correlation between monthly precipitation and malaria cases across a subset of districts showed high levels of correlation at both 1-month and 2-month lags (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The spatial distribution of the correlation coefficients for malaria cases and malaria hospitalizations showed increased correlation across Mozambique for 1-month and 2-month lags (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The spatial distribution of the correlation coefficients for malaria deaths did not show substantial spatial heterogeneity and this did not change with different temporal lags (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When examining the time-series plots of precipitation and malaria cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), there was evidence of a clear temporal relationship throughout Mozambique.\u003c/p\u003e\u003cp\u003eHeat maps of the association between precipitation and the relative risk of malaria prevalence were created for all of Mozambique, and stratified by region (North, Central, and South) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For the entire country, there was a clear increased relative risk for malaria at an 8-week temporal lag, which matches with the overall 2-month lag seen previously. However, this varied when stratified by region. In the Northern Region there was an increased relative risk for malaria between 6-week and 8-week temporal lags. In the Central region there was an increased relative risk for malaria between 10-week and 12-week temporal lags. In the Southern region there was an increased relative risk for malaria between 10-week and 13-week temporal lags.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study we found strong evidence of spatial and temporal correlation between precipitation and malaria risk. When we investigated the association between precipitation and malaria risk at the national level, our results were consistent with the current literature. These results indicated an approximate 8-week lag between elevated precipitation and increased malaria risk. However, when we stratified this by region of Mozambique, we found heterogeneity in these lag periods.\u003c/p\u003e\u003cp\u003eWe found that in the Northern region of Mozambique, this lag is slightly shorter, between 6\u0026ndash;8 weeks. In the Central region of Mozambique we found the lag to be between 8\u0026ndash;10 weeks. And in the Northern region of Mozambique the lag was 10\u0026ndash;13 weeks. While all these values fall within ranges previously described in other locations, they represent important differences for both further modeling and decision making in Mozambique.\u003c/p\u003e\u003cp\u003eIn Mozambique at the national level Armando et al. used a 1\u0026ndash;2 month lag for precipitation, which is consistent with the 8-week lag that we found nationally [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In a study in neighboring Zimbabwe by Gunda et al. a 4-week lag was used for the study area of interest [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In Ghana Krefis et al. found a 9-week lag to be most appropriate for their study area [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These findings, along with others show that there is heterogeneity between countries and study areas in the appropriate lag time in the association between precipitation events and malaria risk.\u003c/p\u003e\u003cp\u003eOur study bolsters the current literature by investigating the specifics of the temporal lag in the association between precipitation and malaria risk in Mozambique. We have shown that at the national aggregate the lag time is consistent with other studies, while disaggregated to regions the lag times vary. These findings are important for informing modeling of climate and environment and malaria risk. Many studies use only aggregate data to determine lag times for models when there is geographic variability to also account for. Additionally, these findings are important programmatically for planning and responding to elevated precipitation events and severe storms. Knowledge of the specific lag times for different regions of Mozambique can be used to respond in a timely manner to severe storms, particularly in planning when to deliver interventions after a storm to have the maximum impact.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWith increasing storms and changing weather patterns due to climate change there is a need to understand the specifics of the association between elevated precipitation and malaria risk. We found important spatial heterogeneity in temporal lag times between precipitation events and malaria risk in Mozambique. These findings provide important information for further modeling and planning of the timing of interventions in response to storms and seasonal precipitation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was funded by the Wellcome Trust program \u0026ldquo;Digital Tools to Transform Infectious Disease Modelling\u0026rdquo; [226053/Z/22/Z]\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHZ conducted the analyses and wrote the first draft of the manuscript. LZ assisted in the design and supervised the analysis. JS supervised the database creation and merging of datasets. MC compiled the database. BC oversaw the malaria surveillance program. MS compiled and organized the malaria surveillance data. KMS designed the study, oversaw the analysis, and finalized the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eOur team would like extend our gratitude to the large network on malaria scientists who gathered and assembled surveillance data at the local, district, provincial and national levels in Mozambique. We also would like to thank colleagues at Navitas Group Global in Mozambique who assisted in the data transfer arrangements.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used for this study were provided by the National Malaria Control Program in Mozambique. Requests for those data should be made to the Mozambique Ministry of Health.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. World Malaria Report 2024. World Health Organization: Geneva, Switzerland; 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO. World Malaria Report 2023. Switzerland: Geneva; 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO. \u003cem\u003eWHO recommends R21/Matrix-M vaccine for malaria prevention in updated advice on immunization\u003c/em\u003e. 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFormenti B, et al. The impact of COVID-19 on communicable and non-communicable diseases in Africa: a narrative review. Infez Med. 2022;30(1):30\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbiodun GJ, et al. Exploring the Impact of Climate Variability on Malaria Transmission Using a Dynamic Mosquito-Human Malaria Model. Open Infect Dis J. 2018;10:88\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlimi TO, et al. Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population. Parasit Vectors. 2015;8:431.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArndt C, et al. Adapting to climate change: an integrated biophysical and economic assessment for Mozambique. Sustain Sci. 2011;6(1):7\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBabaie J, et al. A systematic evidence review of the effect of climate change on malaria in Iran. J Parasit Dis. 2018;42(3):331\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouma MJ, et al. Global malaria maps and climate change: a focus on East African highlands. Trends Parasitol. 2011;27(10):421\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaminade C, et al. Impact of climate change on global malaria distribution. Proc Natl Acad Sci U S A. 2014;111(9):3286\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDasgupta S. Burden of climate change on malaria mortality. Int J Hyg Environ Health. 2018;221(5):782\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHales S, Woodward A. Climate change will increase demands on malaria control in Africa. Lancet. 2003;362(9398):1775.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRomanello M, et al. The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action. Lancet. 2024;404(10465):1847\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRomanello M, et al. The 2023 report of the Lancet Countdown on health and climate change: the imperative for a health-centred response in a world facing irreversible harms. Lancet. 2023;402(10419):2346\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgyekum TP et al. A Systematic Review of the Effects of Temperature on Anopheles Mosquito Development and Survival: Implications for Malaria Control in a Future Warmer Climate. Int J Environ Res Public Health, 2021. 18(14).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNissan H, Ukawuba I, Thomson M. Climate-proofing a malaria eradication strategy. Malar J. 2021;20(1):190.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarques R, et al. Climate change impacts on Anopheles (K.) cruzii in urban areas of Atlantic Forest of Brazil: Challenges for malaria diseases. Acta Trop. 2021;224:106123.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChaturvedi S, Dwivedi S. Understanding the effect of climate change in the distribution and intensity of malaria transmission over India using a dynamical malaria model. Int J Biometeorol. 2021;65(7):1161\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMihailovic DT, et al. Assessment of climate change impact on the malaria vector Anopheles hyrcanus, West Nile disease, and incidence of melanoma in the Vojvodina Province (Serbia) using data from a regional climate model. PLoS ONE. 2020;15(1):e0227679.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSearle KM, et al. Household storm damage limits use of and access to insecticide treated bednets in Mozambique. Ann Hum Biol. 2025;52(1):2461145.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu M et al. Response of Extreme Rainfall for Landfalling Tropical Cyclones Undergoing Extratropical Transition to Projected Climate Change: Hurricane Irene (2011). Earths Future, 2020. 8(3): p. e2019EF001360.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen J et al. \u003cem\u003eImpacts of climate change on tropical cyclones and induced storm surges in the Pearl River Delta region using pseudo-global-warming method.\u003c/em\u003e Sci Rep, 2020. 10(1): p. 1965.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVaro R, Rodo X, Bassat Q. Climate change, cyclones and cholera - Implications for travel medicine and infectious diseases. Travel Med Infect Dis. 2019;29:6\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHope M. Cyclones in Mozambique may reveal humanitarian challenges of responding to a new climate reality. Lancet Planet Health. 2019;3(8):e338\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSriver RL. Climate change: Man-made cyclones. Nature. 2011;479(7371):50\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSriver RL. Climate change: Tropical cyclones in the mix. Nature. 2010;463(7284):1032\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFocus Adriano L, Nazir A, Uwishema O. The devastating effect of cyclone Freddy amidst the deadliest cholera outbreak in Malawi: a double burden for an already weak healthcare system-short communication. Ann Med Surg (Lond). 2023;85(7):3761\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCambaza E et al. Outbreak of Cholera Due to Cyclone Kenneth in Northern Mozambique, 2019. Int J Environ Res Public Health, 2019. 16(16).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePNCM, \u003cem\u003eStrategic Malaria Plan 2017\u0026ndash;2022\u003c/em\u003e. 2017, Republic of Mozambique Ministry of Health: Maputo, Mozambique.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUSAID, \u003cem\u003eDisaster Response - Mozambique\u003c/em\u003e. 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWarren M. Why Cyclone Idai is one of the Southern Hemisphere's most devastating storms. Nature, 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen WH, Azman AS. Mitigating Cholera in the Aftermath of Cyclone Idai. Am J Trop Med Hyg. 2019;101(5):960\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLequechane JD, et al. Mozambique's response to cyclone Idai: how collaboration and surveillance with water, sanitation and hygiene (WASH) interventions were used to control a cholera epidemic. Infect Dis Poverty. 2020;9(1):68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDevi S. Cyclone Idai: 1 month later, devastation persists. Lancet. 2019;393(10181):1585.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTwabi HH, et al. Cyclone Freddy and its impact on maternal health service utilisation: Cross-sectional analysis of data from a national maternal surveillance platform in Malawi. PLOS Glob Public Health. 2024;4(8):e0003565.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCursino M. \u003cem\u003eCyclone Chido kills 94 people in Mozambique\u003c/em\u003e, in \u003cem\u003eBBC News\u003c/em\u003e. 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlowac C, Ferrao JL, Searle KM. The association between infrastructure damage in the aftermath of Cyclone Idai and malaria risk in Sofala Province, Mozambique: an ecological study. Malar J. 2024;23(1):355.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSearle KM, et al. Long-lasting household damage from Cyclone Idai increases malaria risk in rural western Mozambique. Sci Rep. 2023;13(1):21590.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTompkins AM, Caporaso L. Assessment of malaria transmission changes in Africa, due to the climate impact of land use change using Coupled Model Intercomparison Project Phase 5 earth system models. Geospat Health. 2016;11(1 Suppl):380.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArmando CJ, et al. Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018. Sci Rep. 2025;15(1):11971.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGunda R, et al. Malaria incidence trends and their association with climatic variables in rural Gwanda, Zimbabwe, 2005\u0026ndash;2015. Malar J. 2017;16(1):393.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArmando CJ, et al. Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016\u0026ndash;2018: a spatial temporal analysis. Front Public Health. 2023;11:1162535.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrefis AC, et al. Modeling the relationship between precipitation and malaria incidence in children from a holoendemic area in Ghana. Am J Trop Med Hyg. 2011;84(2):285\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Q, et al. Association of temperature and precipitation with malaria incidence in 57 countries and territories from 2000 to 2019: A worldwide observational study. J Glob Health. 2024;14:04021.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"malaria, climate, precipitation","lastPublishedDoi":"10.21203/rs.3.rs-7755148/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7755148/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eClimate change is impacting the seasonal weather trends. Mozambique is one country that has been experiencing significant impacts of climate change. These impacts include changing weather patterns and more frequent and intense storms. Mozambique also has the 5th highest malaria prevalence globally. These storms have all impacted the efficiency of the Mozambique National Malaria Control Program to achieve their goals. The goal of our study was to determine the appropriate temporal lag between high precipitation events and malaria risk in Mozambique. This knowledge is imperative to understand when and how to best respond the impacts of these events.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMalaria monthly case count data at the district level were provided by the Mozambique National Malaria Control Program. Precipitation data were obtained from NASA GIS DISC Earth Data and were spatially and temporally aggregated to the district to match the malaria surveillance data. We investigated the correlation between precipitation and malaria-related values at the district level each month with the spatial and temporal correlations considered. We used Pearson correlation coefficients to quantify the correlations. We used Poisson generalized linear mixed models to determine the relative risk of malaria associated with precipitation nationally and for each of the three regions in Mozambique.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe found evidence of strong spatial variation in malaria cases, malaria hospitalizations, and malaria deaths. There was evidence of a clear temporal relationship throughout Mozambique. Nationally, there was an increased relative risk for malaria at an 8-week temporal lag. However, this varied when stratified by region. In the Northern Region there was an increased relative risk for malaria between 6-week and 8-week temporal lags; in the Central region there was an increased relative risk for malaria between 10-week and 12-week temporal lags; and in the Southern region there was an increased relative risk for malaria between 10-week and 13-week temporal lags.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWith increasing storms and changing weather patterns due to climate change there is a need to understand the specifics of the association between elevated precipitation and malaria risk. We found important spatial heterogeneity in temporal lag times between precipitation events and malaria risk in Mozambique.\u003c/p\u003e","manuscriptTitle":"Exploratory analysis of the correlation between precipitation and storms and malaria prevalence at the national scale in Mozambique","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 15:26:06","doi":"10.21203/rs.3.rs-7755148/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-15T21:26:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-15T21:24:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223819354741438665726782917200225446466","date":"2026-03-01T14:08:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270347216360682603412017803493728736493","date":"2026-01-21T15:10:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198026328698302857977364047781272411349","date":"2025-11-21T19:40:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-08T10:09:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210764453639187508137093841320280412842","date":"2025-10-28T00:12:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27887350965171671037257479086920191067","date":"2025-10-16T09:00:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T23:03:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T01:20:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-06T01:19:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2025-09-30T22:24:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bbe5304f-f33a-4498-b906-1fca77bbea16","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T22:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 15:26:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7755148","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7755148","identity":"rs-7755148","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}