Associations between weather and Plasmodium vivax malaria in an Amazonian elimination setting: a distributed lag analysis from 2017–2024

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Nguyen, Astrid Altamirano-Quiroz, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9418255/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background There is limited evidence regarding the association between weather and Plasmodium vivax ( Pv ), particulary in Latin America where Pv is the predominant malaria species and key challenge for countries to achieve malaria elimination. Methods We analyzed the association between weather and Pv malaria incidence from 2017–2024 in 136 communities in the Peruvian Amazon. Monthly community-level incidence was calculated using Pv case data from Notiweb, the national epidemiological surveillance system, and population census data. Predictors included weekly minimum and maximum temperature and total weekly precipitation and were calculated using hourly weather from the climate dataset ERA5. Non-linear distributed lag models were fit using a lookback period of 2–16 weeks. Temperature models were adjusted for total precipitation; precipitation models were adjusted for maximum temperature. Sub-group analyses were conducted by community type (adjacent to river versus highway) and El Niño Southern Oscillation (ENSO) period. Results Minimum temperature at the 90th percentile (23.7ºC) was associated with 10% (95% CI 5%–14%) higher malaria incidence compared to the 5th percentile (20.5ºC) at a 7-week lag. Maximum temperature at the 90th percentile (33.7ºC) was associated with 10% (95% CI 8%–13%) higher malaria incidence compared to the 5th percentile (29.6ºC) at a 9-week lag. Total weekly precipitation at the 90th percentile (1000mm) was associated with 29% (95% CI 24%–33%) higher malaria incidence compared to weeks with the 5th percentile (57mm) at an 11-week lag. Incidence was higher and associations were stronger in communities adjacent to rivers versus highways. Malaria incidence was lower during El Niño periods, and there was evidence of interaction on the multiplicative scale for the association between incidence, all weather predictors, and ENSO period. Conclusions Pv malaria incidence was positively associated with higher temperatures and precipitation in an elimination setting in Peru, particularly in riverine communities during non-El Niño years, with longer lag periods than previously reported for such associations. These findings can inform malaria elimination interventions to combat the long-lasting effects of weather on Pv transmission. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 BACKGROUND As countries approach malaria elimination, the proportion of malaria infections due to Plasmodium vivax ( Pv ) increases. 1 Pv presents unique challenges for malaria elimination efforts because of its ability to survive in wide ranging environments, including temperate settings, as a result of its dormant hypnozoite stage, which re-emerges to cause relapse infections. 2 Pv only invades reticulocytes, early stage red blood cells, and infections are low density and often missed by standard diagnostics. Further, a high proportion of infections are asymptomatic, and thus evade detection through standard surveillance. These characteristics make Pv difficult to detect, treat, and ultimately eliminate. The relationship between weather and Plasmodium falciparum ( Pf ) malaria transmission is well-documented, leading to routine deployment of seasonal malaria interventions, such as seasonal malaria chemoprevention 3 , in places where Pf is endemic. However, the relationship between weather and Pv malaria transmission, whether seeded by relapses or new infections, is not as well understood, preventing malaria programs from considering seasonal targeting of interventions to optimize their impact. However, there is strong biologic plausibility for temperature and precipitation to influence Pv transmission via similar mechanisms to Pf (Fig. 1 ). 4 – 10 Precipitation can increase standing water presence, increasing breeding ground for mosquitoes 11 ; temperature has strong effects on the lifecycle of the Anopheles vector, including larval development, biting rate, parasite incubation rate, and longevity 12 , 13 . Further, there is emerging evidence that weather could directly trigger relapse Pv infections 14 , and weather may indirectly drive Pv infections via the saliva of uninfected mosquitoes, which contain antigens that may trigger relapse (biting rate is temperature-sensitive) 15 . Further, if seasonal patterns for Pv exist, strong spatial heterogeneity 16 , 17 in endemic regions requires local characterization to inform intervention targeting. Over 90% of Latin America’s malaria burden is concentrated in the Amazon Basin 18 . Parasite populations and vector behavior vary significantly in this region, 19–21 and this variation is likely driven by adaptations to different environmental conditions. 20 , 22 , 23 Prior studies of the relationship between weather and malaria incidence in the Amazon have not focused exlcusively on Pv even though Pv is estimated to account for 77% of the transmission burden. 21 Finally, the Amazon Basin is undergoing rapid environmental and climatic changes, including deforestation, warming, and drought, 24–26 that may alter transmission dynamics at a fine scale, further emphasizing the need for local characterization. Our objective was to assess the influence of rainfall and temperature on Pv incidence in Loreto Region, Peru, where the maximum temperatures fall between 30–35ºC, which are above the estimated thermal optimum for malaria transmission (25ºC) 27 . Daily temperature extremes are thought to limit transmission via detrimental impacts on parasite development, adult mosquito survival, egg survival, and mosquito development 27 – 29 . However, to our knowledge, the thermal biology of Pv transmission by Nyssorhynchus (formerly Anopheles ) darlingi , the primary vector in this region, has also not been well characterized by laboratory thermal biology studies. Information about climatic drivers of Pv transmission is needed to inform effective tailoring and targeting of interventions for malaria elimination in this region. METHODS Study design We analyzed malaria incidence data collected from January 2017 to December 2024 in all 136 communities within 8 hours of Iquitos, the periurban center of Loreto Region, Peru. This area covering 2,569 km 2 includes the districts of San Juan Bautista, Punchana, and Alto Nanay (Fig. 2 ). The Peruvian Ministry of Health (MINSA) conducted a census in 2017 that was used to estimate the starting population size of each community. The average population size of each community was 321 individuals (range: 15 to 3223). The Loreto Region in Peru includes a large swath of the Peruvian Amazon. 30 Since the 1990s, rapid urbanization and deforestation in the area surrounding Iquitos, the capital of the Department of Loreto, has contributed to increased malaria burden 30 , likely through increased forest edge habitat, which promotes mosquito breeding, survival, and biting 24 . While this region is approaching elimination, sustained, endemic transmission remains, with an annual incidence rate of 17.4 cases per 1000 in 2019 (annual incidence < 100 per 1000 is considered very low transmission 31 ), 32 and Pv malaria accounts for approximately 80% of the malaria burden. 30 Transmission typically peaks in Loreto between February and July 30 and there is a sizable burden of asymptomatic infections. 6 Asymptomatic infections typically go undetected and uncounted in routine malaria surveillance in Peru, 6 since only febrile individuals are tested. From 2005–2010, there was a large effort towards malaria elimination in Peru via the Project for Malaria Control in Andean Border Areas (PAMAFRO) program that improved case management, including through deployment of community case workers and distribution of insecticide-treated bed nets. 30 Transmission declined during the project, but the project did not achieve elimination, and transmission has since risen. 33 Following PAMAFRO, the government of Peru adopted Plan Malaria Cero (PMC) in 2017 to achieve malaria elimination by 2030. Activities conducted over the study period as part of Plan Malaria Cero include strong case management of suspected malaria cases presenting to health facilities, distribution of long-lasting insecticide-treated bed nets (ITN), and outbreak responsive activities such as active case detection (ACD), fogging, and indoor residual spraying 34 . Outcome data The primary outcome was weekly community-level Pv malaria incidence measured in surveillance data. Data primarily included passive case detection, though ACD and RACD also occurred over the study period and were recorded in surveillance data. Both physical records stored in health posts and online records compiled by MINSA were used in this analysis. Febrile patients presented for care at a health post and were tested for malaria via microscopy. According to PMC policy 34 , blood smears are stained with 2% Giemsa for 30 minutes. Parasite densities are calculated from the number of asexual parasites per 200 leukocytes (or per 500, if < 10 asexual parasites/200 leukocytes), assuming a leukocyte count of 8,000/µL. A blood smear is considered negative if examination of 100 high power fields does not reveal asexual parasites. Thin smears are used for parasite species identification. Slides are read by two microscopists. If there are discordant results, the results are determined by a third microscopist. Only Pv cases were included in the present study, and we were unable to distinguish between primary and relapse infections from microscopy data. Cases were matched to community census data to confirm community of residence and then matched to geocoordinates for the community centroid. Population counts per community were calculated from a government census conducted from 2017. The FocaL Mass Drug Administration for Vivax Malaria Elimination (FLAME) 35 trial in Peru (NCT05690841) conducted a census on a subset of 30 communities in 2023. We estimated adjusted population counts per year by calculating the annual growth rate using the 2023 and 2017 census counts in the subset. The same flat annual growth rate was applied to all 136 communities with a population greater than 15 people in the study area. We obtained data for ITN distribution from MINSA with dates of delivery for communities included in the study from 2018–2024. Time since most recent ITN delivery was discretized into never received, 0–1 year, 1–3 years, and more than 3 years. Environmental variables We obtained temperature and precipitation data from the ERA5-Land Hourly dataset, 11 kilometer resolution, collected by the Copernicus Climate Change Service 36 . We used the air temperature at 2 meters above the surface (K) band for all temperature variables and the total precipitation (m) band for precipitation variables. We matched weather variables to incidence data via community centroid. There was no missing weather data over the study period. We aggregated hourly temperature values into the weekly minimum and weekly maximum temperature; and total precipitation observed each week. All data extraction was performed using the Python API for Google Earth Engine on Google Colab servers. Aggregation over time and statistical modeling were performed in R version 4.2.1. Furthermore, the Amazon region is subject to longer-term climate oscillations due to the El Niño Southern Oscillation (ENSO). The ENSO cycle is characterized by hot, dry conditions during El Niño years and rainy conditions during La Niña years; these periods oscillate with neutral periods every 3–7 years27. We obtained data on the Oceanic Niño Index (ONI), a measure of the strength of the El Niño-Southern Oscillation (ENSO), from the National Oceanic and Atmospheric Association National Weather Service Climate Prediction Center. A 1-unit increase in ONI represents a 1ºC higher average sea-surface temperature compared to a 30-year reference period 37 . We manually designated riverine versus highway communities via Google Earth hybrid satellite image using QGIS version 3.32. We assigned communities with no land connections to the Iquitos-Nauta highway as riverine, and all other communities as highway. We checked our designations with ground-truth observations from 2023 for the FLAME community subset. Statistical analysis We published a pre-analysis plan at https://osf.io/f89te . Deviations from the pre-analysis plan are listed in Table S1. We removed years 2020 and 2021 from our primary analysis because of strong changes in incidence and case detection resulting from behavior changes and health system capacity during the COVID-19 pandemic. To assess the timing of the association between malaria incidence and weather variables, we we fit distributed lag models. We chose a 2–16-week lookback period (i.e., an infection during epidemiologic week 20 looked back to weeks 4, 5, …, 18) to account for variation in the potential cumulative effect of rainfall and temperature on Pv transmission and long-lasting impacts on relapse cases (Fig. 1 ). We set the upper limit at 16 weeks (4 months) to ensure we captured at least one relapse cycle in accordance with studies of tropical strains of Pv documenting median time to first relapse 6–9 weeks after an initial infection 4 and frequent relapses 3–4 weeks apart for up to 4 months after the initial infection 9 . We were unable to adjust for season over our study period, as data-based definitions (i.e., consecutive weeks with rolling average of precipitation above a percentile cutoff) failed to delineate clear seasons. Given prior literature states seasons generally last for 6 months 30 , constraining our lookback period to 4 months also limited the effects of seasonal confounding. For weather exposures (weekly minimum temperature, weekly maximum temperature, and total precipitation), we fit non-linear distributed lag (DL) models 38 with a log link and Poisson family using weekly malaria case counts per community as the dependent variable with an offset for log community population size using the R package DLNM version 2.4.7 39 . For the distributed lag cross-basis function, we specified natural splines with 2 df for the exposure-response dimensions, and 1 internal lag knot to allow for differences in early versus later lag periods. This combination best balanced flexibility with stability compared to specifications with higher df values (3 or 4) and for the natural splines and knot values for the lags, which showed evidence of overfitting. All incidence ratios were calculated relative to reference weeks. In all models, we restricted weather measurements in the input data to observations between the 5th and 95th percentile of the variable’s empirical distribution to remove outlier effects. For temperature predictors, we used 20.5ºC for minimum temperature and 29.6ºC for maximum temperature as references. These values were selected because they correspond to the minimum value within the data range during the study period. For total precipitation, weeks with 57 mm of precipitation were used as the reference. Temperature variables were adjusted for the cross-basis matrix for total precipitation, and total precipitation was adjusted for the cross-basis matrix for minimum temperature, in accordance with AIC/BIC model testing. All DL models included fixed effects for year, community, and years since ITN distribution, and a smooth term for ONI with 3 knots. All positive cases were matched to weather data. Thus, we performed a complete case analysis. Because incidence data for the weather models was aggregated at the community level, we did not test for spatial autocorrelation or adjust for spatial clustering. To assess the relationship between each predictor value and malaria incidence, we fit generalized additive models (GAMs) with a Poisson family and log link and an offset for community population size. We fit models for the week with the strongest association in the DL model (primary lag). All GAMs included fixed effects for year, community, and years since ITN delivery and a smooth term for ONI with 3 knots. We derived confidence intervals from the 2.5th and 97.5th percentiles of a 1,000-replicate bootstrap distribution. For each bootstrap replicate, we used g-computation to fix the predictor at each point across its observed range and average model predictions across all observed covariate combinations. We conducted subgroup analyses using the primary lag for temperature and precipitation predictors stratifying by ENSO periods and community type. We defined periods of 5 or more consecutive months of sea surface temperature abnormality of 0.5ºC or above/-0.5ºC or below as El Niño/La Niña periods, respectively 37 . To assess interaction effects between weather and ENSO period or community type on the multiplicative scale, we fit generalized linear models (GLMs) that included the same covariates from GAM models as fixed effects. RESULTS During the study period from January 2017 to December 2024, minimum temperature varied from 17–25ºC, and maximum temperature varied from 28–41ºC (Fig. 3 A). Total weekly precipitation varied from 7mm per week to 2,587mm per week (Fig. 3 B). There were 112 El Niño weeks, 158 La Niña weeks, and 147 neutral weeks over the study duration, and incidence varied by ENSO period, with the highest incidence during neutral periods. Malaria incidence was highest from 2017–2019, then decreased dramatically in 2020 and 2021 and increased to levels below the 2019 levels in 2022–2024. Malaria incidence did not appear to follow a distinct seasonal trend (Fig. 3 C). The mean incidence for all communities was 10.4 cases per 10,000 person-weeks. Temperature There was a positive association between minimum temperature and malaria incidence at 2–13 weeks lags, and a negative association at 14–16-week lags when comparing the 90th percentile of minimum temperature (23.7ºC) to the 5th percentile (20.5ºC) (Fig. 4 A, 4 C). Where this association was highest, at a 7-week lag, the association between minimum temperature and incidence was positive and nonlinear (Fig. 4 B). At minimum temperatures of 20.5–22ºC, incidences were similar. At higher temperatures, there was a positive, non-linear association with estimated incidence of 18 cases per 10,000 at 20.5ºC compared to 30 cases per 10,000 at 23.8ºC. Compared to weeks with the lowest minimum temperature, weeks with a minimum temperature of the 90th percentile were associated with up to 10% higher malaria incidence (95% CI 6%–14%), or incidence ratio of 1.10 at a 7-week lag (Fig. 4 A). The association between maximum temperature and malaria incidence was non-linear and hump-shaped across lag time; there was a positive association between maximum temperature and malaria incidence at 4–14-week lags when comparing the 90th percentile of maximum temperature (33.7ºC) to the 5th percentile (29.6ºC) (Fig. 4 D, 4 F). Where this association was highest, at a 9-week lag, incidence increased approximately linearly from an estimated 16 cases per 10,000 to 26 cases per 10,000 as maximum temperature increased from 29.6ºC to 34ºC (Fig. 4 E). Compared to weeks with the lowest maximum temperature, weeks with a maximum temperature of the 90th percentile were associated with up to 12% higher malaria incidence (95% CI 9%–15%), or incidence ratio of 1.12 at a 9-week lag (Fig. 4 D). Precipitation The association between precipitation and malaria incidence was non-linear and positive at lags of 5–16 weeks, when comparing the 90th percentile of precipitation (1000mm) to the 5th percentile (57mm) (Fig. 4 G, 4 I). Where this association was highest, at an 11-week lag, incidence increased approximately linearly from an estimated 18 cases per 10,000 to 24 cases per 10,000 as precipitation increased from 57mm to 1161mm (Fig. 4 H). At an 11-week lag, compared to weeks with no precipitation, weeks with precipitation at the 90th percentile were associated with up to 25% higher malaria incidence (95 CI 21%–28%), or incidence ratio of 1.25 (Fig. 4 G). Community Type Sub-group Malaria incidence was higher in riverine versus highway-adjacent communities (Table 1 , Fig. 5 A–C). For minimum temperature, GLMs showed that associations were stronger in riverine versus highway-adjacent communities: the incidence ratio for a 1ºC increase in minimum temperature was 1.27 (95% CI 1.23–1.31) in riverine communities compared to 1.10 (1.02–1.19) in highway communities (interaction p = 0.001) (Table 1 ). For maximum temperature, GLMs showed that associations were stronger in riverine versus highway-adjacent communities: the incidence ratio for a 1ºC increase in maximum temperature was 1.13 (95% CI 1.11–1.15) in riverine communities compared to 1.03 (0.98–1.09) in highway communities (interaction p = 0.001) (Table 1 ). For precipitation, GLMs showed associations were weaker for riverine versus highway-adjacent communities: the incidence ratio for a 500mm increase in precipitation was 1.04 (95% CI 1.00–1.08) in riverine communities compared to 1.59 (95% CI 1.44–1.76) in highway communities (interaction p < 0.001) (Table 1 ). Table 1 Plasmodium vivax malaria incidence ratios across temperature and precipitation, by community type. We fit generalized linear models (GLMs) with interaction terms to obtain incidence ratios across temperatures and precipitation values. Treating highway communities as the reference, p values for interaction are shown. Temperature GLMs adjusted for precipitation at a concurrent lag, while precipitation models adjusted for minimum temperature at the concurrent lag. All GLMs were adjusted for community ID, year, Oceanic Niño Index, and years since insecticide-treated bednet delivery. All temperature ranges are reported in ºC. Precipitation ranges are reported in millimeters. Incidence ranges are reported per 10,000 person-weeks. Incidence ratios are reported per 1ºC increase for temperature predictors and per 500mm increase for precipitation. Community type Predictor range Incidence range Incidence Ratio (95% CI) Interaction p-value Minimum temperature Highway 20.5–23.9 7.3–13.5 1.10 (1.02–1.19) -- Riverine 20.4–23.8 17.8–31.4 1.27 (1.23–1.31) 0.001 Maximum temperature Highway 29.5–33.9 5.2–10.8 1.03 (0.98–1.09) -- Riverine 29.6–34.0 17.4–27.1 1.13 (1.11–1.15) 0.001 Precipitation Highway 51–1139 7.7–16.3 1.59 (1.44–1.76) -- Riverine 57–1161 18.3–23.0 1.04 (1.00–1.08) < 0.001 ENSO Sub-group Malaria incidence was highest during neutral periods and lowest during El Niño periods (Table 2 , Fig. 5 D–F). Using GLMs, associations between minimum temperature and malaria incidence were positive when stratifying by ENSO period and strongest during neutral and El Niño periods. The incidence ratio for a 1ºC increase in minimum temperature was 1.34 (95% CI 1.28–1.39) during neutral periods, 1.28 (95% CI 1.21–1.36) during El Niño periods (interaction p = 0.269), and 1.04 (95% CI 0.99–1.10) during La Niña periods (interaction p < 0.001) (Table 2 ). Associations between maximum temperature and malaria incidence were positive when stratifying by ENSO period and strongest during neutral and El Niño periods. The incidence ratio for a 1ºC increase in maximum temperature was 1.14 (95% CI 1.11–1.18) during neutral periods, 1.12 (95% CI 1.08–1.16) during El Niño periods (interaction p = 0.38), and 1.06 (95% CI 1.03–1.09) during La Niña periods (interaction p < 0.001) (Table 2 ). Associations between total precipitation and malaria incidence were positive when stratifying by ENSO period and somewhat stronger during La Niña periods. The incidence ratio for a 500mm increase in precipitation was 1.06 (95% CI 1.01–1.12) during neutral periods, 1.07 (95% CI 0.99–1.15) during El Niño periods (interaction p = 0.973), and 1.13 (95% CI 1.06–1.20) during La Niña periods (interaction p = 0.183) (Table 2 ). Table 2 Plasmodium vivax malaria incidence ratios for malaria incidence across temperature and precipitation, by ENSO (El Niño-Southern Oscillation) period. We fit generalized linear models (GLMs) with interaction terms to obtain incidence ratios across temperature and precipitation values. Treating normal ENSO periods as the reference, p values for interaction are shown. Temperature GLMs adjusted for precipitation at a concurrent lag, while precipitation models adjusted for minimum temperature at the concurrent lag. All GLMs were adjusted for community ID, year, Oceanic Niño Index, and years since insecticide-treated bednet delivery. All temperature predictor ranges are reported in ºC. Precipitation predictor ranges are reported in millimeters. Incidence ranges are reported per 10,000 person-weeks. Incidence ratios for temperature predictors are reported per 1ºC increase and per 500mm increase for precipitation. ENSO period Predictor range Incidence range Incidence Ratio (95% CI) Interaction p-value Minimum temperature Normal 19.9–23.7 17.3–31.7 1.34 (1.28–1.39) -- El Niño 21.4–24.0 4.3–16.7 1.28 (1.21–1.36) 0.269 La Niña 20.2–23.4 14.2–20.2 1.04 (0.99–1.10) < 0.001 Maximum temperature Normal 29.5–33.7 20.4–25.9 1.14 (1.11–1.18) -- El Niño 30.0–34.4 8.5–15.5 1.12 (1.08–1.16) 0.38 La Niña 29.4–33.6 16.4–20.0 1.06 (1.03–1.09) < 0.001 Precipitation Normal 31–1104 20.3–24.6 1.06 (1.01–1.12) -- El Niño 84–1244 10.3–15.4 1.07 (0.99–1.15) 0.973 La Niña 93–1035 16.6–19.6 1.13 (1.06–1.20) 0.183 DISCUSSION Higher temperatures were associated with modestly higher Pv malaria incidence, and total precipitation was associated with moderately higher incidence in an elimination setting in Peru. Associations generally lasted several weeks, with more sustained, stronger associations for precipitation. We only observed associations between malaria incidence and minimum temperatures above 22ºC, suggesting that cooler minimum temperatures may be a limiting factor for Pv malaria incidence. Incidence was lowest during the dry El Niño periods and higher in wetter La Niña and neutral periods. For temperature predictors, associations were stronger during neutral and El Niño periods while for precipitation, associations were somewhat stronger during La Niña periods. Overall, periods of higher temperatures and cumulative rainfall were associated with increased malaria incidence in the subsequent several weeks. Our findings suggest that weather influences malaria burden over long, 1–4-month lags in this region. The length of this association likely reflects the impact of weather on vector population dynamics and biting behavior to initate multiple transmission cycles, as well as the potentially overlooked relapse periodicity of Pv malaria. The efficacy of standard of care anti-relapse treatment is approximately 75–80%, 40 but effectiveness can be significantly lower due to challenges with adherence. 41 , 42 Further, many infections go undetected and untreated due to limited sensitivity of diagnostics, healthcare access challenges, or minimal symptoms which precludes health seeking. As such, the vast majority of Pv infections are estimated to be relapse infections, 43,44 and incidence measured in this study included both new and relapse infections. Additionally, relapses from the tropical strains of Pv are estimated to range anywhere from 2 weeks 45 to 2–9 months, 9 supporting the duration of our lagged findings. Precipitation We found that higher precipitation was significantly associated with higher malaria incidence at lags of 5–16 weeks. Our findings aligned with other studies of rainfall and Pv malaria focused on temperate and drier settings at similar lags. A meta-analysis in Mauritania found that Pv incidence was highest during and after the rainy season 46 and that decreased rainfall was significantly correlated with decreased malaria burden. A study in South Korea, a temperate area, found that increased precipitation was associated with higher malaria incidence at a 10-week lag. 47 A similar study in a tropical area of China also found positive associations with precipitation at lags 2–4 weeks. 48 A study of Amazon border regions found that Pv was negatively associated with precipitation at a 6-month lag 49 ; however, the study did not investigate the shorter-term weather influences studied here. The lasting influence of precipitation in our findings likely reflects increased breeding ground following rainfall, which could impact multiple transmission cycles. The 5–16 week lag may also include the transition from the dry season, when the vector can still thrive in moist environments. Temperature We found that higher minimum temperatures and higher maximum temperatures were both associated with higher malaria incidence from 4–11-week lags and 4–14-week lags, respectively. The results for minimum temperature were largely in accordance with prior literature, while the results for maximum temperature were surprising. This mixed support for our findings may reflect the substantial geographic diversity and heterogeneity of the Amazon region 18 – 20 , 22 , 50 , 51 . Regarding vector ecology, though the thermal transmission optimum of the Pv – Ny. darlingi coupling has not been studied directly, prior studies suggest that Pf and Pv malaria transmission peaks at 25ºC and is limited above 30ºC 13 . One study found the thermal optimum for malaria vectors is 25ºC with an upper limit of 33ºC, 12 while another found that Ny. darlingi adult lifespan and body size declined and development rate increased as temperatures increased from 20ºC to 28ºC. Therefore, the vector ecology literature supports our finding of positive associations with minimum temperature, which ranged from 20–24ºC, but contrasts with our findings of positive associations with maximum temperatures, which ranged from 30–34ºC. One possible explanation for the conflict with maximum temperature is vector biting behavior: Ny. darlingi typically bites humans at dusk or overnight, 52,53 and the highest temperatures occur in the afternoon. However, this explanation does not account for the expected declines in vector populations above 30ºC, driven by the negative impacts on larval development, reproduction, and adult longevity above such temperatures 12 , 54 . An alternative explanation is that microclimates, topographical variation, and forest cover may moderate higher temperatures in our study site. Given that we used data with 11 kilometer resolution, our findings could not account for such fine scale phenomena. Finally, the vector in our study sites may be adapted to higher maximum temperatures 55 to prevent population decline, given strong microclimate heterogeneity 20 in the Amazon. Our findings also have mixed support in the empirical Pv literature in similar settings. A study of Amazon border regions found that Pv incidence was negatively associated with minimum temperatures between 17–25ºC at 0- and 6-month lags, in contrast to our findings for minimum temperature, but positively associated with maximum temperatures between 26.8–35.2ºC 1- and 2-month lags 49 , in accordance with our findings for maximum temperature. A study in neighboring Brazil found that mean temperatures between 25–31ºC were associated with higher malaria risk at 1-week lag but that mean temperatures above 25ºC were associated with lower malaria risk 17 at 2- and 3-week lags. However, the study in Brazil used mean temperature, thereby preventing comparisons with our daily extreme measures, and both studies used different modeling structures and lookback periods, preventing direct comparisons. Given that the majority of incident Pv is thought to be relapse, 44 one additional potential explanation for the contrasting associations with temperature may be related to the unexplored relationship between environmental triggers and Pv relapse immunology. Though precise evidence is limited, extrinsic triggers of Pv relapse may include co-infection with P. falciparum 56 or other febrile illnesses 4 that result in host inflammation, subsequent primary Pv infections, seasonal changes in sunlight and temperature, and mosquito bites and their associated immune responses. 14 Pv relapse is common following a Pf infection 4 , but Pf infections are uncommon in our study site. Dengue, however, is increasingly common, 57 and its transmission is positively associated with maximum temperatures similar to those in our study 58 – 60 and has a higher thermal optimum (29ºC) and upper limit (34.5ºC) than malaria. 12 Co-infections with dengue could thus occur at higher temperatures and trigger Pv relapses via a similar mechanism that Pf infections are thought to activate. It is also possible that higher temperatures lead to host inflammation and heat stress that activate the hypnozoite. ENSO sub-group analyses We found that incidence was generally lowest during El Niño periods compared to neutral or La Niña periods, when incidence was similar. There is mixed support for this finding in the literature. One study in neighboring Colombia found that caseloads were higher during El Niño events 61 , possibly because higher temperatures and lower rainfall lead to decreased river discharge and water stagnation, creating an optimal malaria breeding ground. A study in the Brazilian Amazon found that incidence was lower during El Niño and La Niña periods 17 , somewhat contradicting our results. The rivers in our study region tend to be shallow and small, and some dry up completely during strong El Niño periods, such as in August 2023. Additionally, the Amazon has been undergoing historic drought due to global climate change 26 , with 2023 being a particularly pronounced drought year. Climate change may be dramatically altering the effects of weather independently and on a longer time scale than any individual ENSO period studied here. Drought brought on by El Niño and climate change may decrease the availability of stagnant pools of water necessary for mosquito breeding, explaining the overall lowest incidence during El Niño periods. Additionally, temperatures were highest during El Niño periods, potentially limiting mosquito activity. On the other hand, because La Niña periods are associated with wetter conditions, 62 in the context of historic drought that has dried up rivers in our study region, increases in rainfall could potentially create temporary pools of stagnant water, explaining the higher incidence during these periods in our study region. We also found evidence of interaction between La Niña periods and weather predictors; associations between malaria incidence and precipitation were strongest during La Niña periods, while associations between malaria incidence and temperature were weakest during La Niña periods. These interaction effects suggest that variation in precipitation drives malaria transmission during La Niña periods, while during normal and El Niño periods, temperature largely drives transmission. Community type sub-group analysis Incidence was higher in river- versus highway-adjacent communities, and the association with maximum temperature was also stronger in riverine communities. On the other hand, the association with precipitation was stronger in highway-adjacent communities. It is possible that rainfall is more likely to create stagnant pools of water in highway-adjacent communities than riverine communities, where water bodies are larger and rainfall may result in a more modest increase in ideal water bodies for mosquito breeding. Conversely, in river-adjacent communities where mosquitoes can breed uninhibited, temperature may be more limiting, explaining the stronger association with maximum temperature in these communities. In addition, higher vegetation near rivers may create cooler microclimates that approximate the malaria thermal optimum better than more urbanized highway communities, which may be hotter. Limitations Our study had several limitations. We were unable to distinguish between initial and relapse infections in our incidence data, a limitation shared with prior observational studies of Pv 63,64 . We would expect that weather would influence initial and relapse cases over different lag periods, but our analysis was not able to investigate this. A follow-up study using genomic methods or a longitudinal cohort study with temporally dense testing to differentiate between initial and relapse infections could elucidate if weather differentially influences primary versus relapse infections. We were also unable to distinguish which cases were identified via passive versus active case detection, which were done as malaria control efforts by the Peruvian Ministry of Health, greatly affecting case detection and potentially creating bias in our results. However, these interventions were rare and generally done as a reaction to higher passively detected caseloads, suggesting that passive surveillance-detected incidence was already higher when active case detection was done. The study period also included two years (2021 and early 2022) where control efforts for the COVID-19 pandemic likely limited malaria transmission as well. While we selected ERA-5 Land remote sensing data for its temporal coverage and lack of missingness, its 11 km spatial resolution prevented identification of small-scale microclimates, such as those created by forest cover and topographical features, and thus did not capture small-resolution variation that may have a large effect on mosquito breeding and survival habitat. 65 Fine-resolution drone imagery, 65 as used in other studies, may help overcome this limitation. While we initially considered land cover variables, they were limited in statistical power. Additionally, publicly available surface water data did not reflect our ground observations of surface water in the study site, so we did not include it in this analysis. Further research that considers the influence of non-weather environmental covariates could help elucidate the complicated, heterogeneous dynamics of malaria transmission in this region, and consider mediation under changing climatic conditions. This study was also correlational, and inferred relationships with individual weather variables may be confounded by collinearity and correlation with other weather variables and thus made it difficult to tease out individual direct effects. CONCLUSIONS In our study of malaria incidence in Amazonian Peru, we observed generally positive associations with higher temperatures and higher rainfall for extended lag periods beginning 4–5 weeks after an initial weather event and enduring for 1–4 months. We also found lower incidence during El Niño years and higher incidence in riverine communities, which may be used to concentrate resources and time malaria interventions to achieve elimination in this setting. Our findings indicated that malaria burden in this region may be positively associated with higher temperatures. As the successes or failures of malaria interventions may be in part due to weather initiating long-lasting Pv malaria cycles, these findings provide critical context to ongoing malaria elimination efforts in the Amazon region. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of Stanford University (72291) and by the Dirección Universitaria de Asuntos Regulatorios de la Investigación de la Universidad Peruana Cayetano Heredia (211747). Consent for publication Not applicable Availability of data and materials All code and public data are available to replicate the analysis, figures, and tables on GitHub: https://github.com/gabbyrbh/vivax-env-rf-public. Public data are available in the 6-public-data/output/ folder. Community ID numbers have been scrambled in the public data. Data to complete the processing and weather extraction steps are private because they contain village geocoordinate and name data, but may be available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding We received funding from the National Institute of Allergy and Infectious Disease, GSK, and the King Center on Global Development. Funding sources did not contribute to the study design, data collection, analysis, writing, or decision to submit for publication. Authors’ contributions GBH: Methodology, Software, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Visualization XW: Resources, Data Curation ATN: Methodology, Validation, Writing – Review & Editing AAQ: Investigation, Resources SRF: Investigation, Resources, Data Curation, Project administration, Writing – Review & Editing BFC: Investigation, Resources Antony B: Investigation, Resources, RC: Investigation, Resources, Data Curation VC: Resources, Supervision HR: Resources, Supervision GCE: Investigation, Resources, Methodology Adam B: Writing – Review & Editing ALC: Resources, Supervision, Funding Acquisition EAM: Writing – Review & Editing MSH: Conceptualization, Funding Acquisition, Writing – Review & Editing JBC: Conceptualization, Methodology, Funding Acquisition, Validation, Writing – Review & Editing Acknowledgements MH and JBC are Chan Zuckerberg Biohub Investigators. References Kattenberg JH, Erhart A, Truong MH, et al. 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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-9418255","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633758066,"identity":"e15ea565-5973-4959-af7d-e11528c3fa27","order_by":0,"name":"Gabriella Barratt Heitmann","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYFACHjYgcSCBH8yxAbEZGCSI0iLZwAyk00jRYnCAWC3y7mePPebdcyfP+Pz5g58LEmzy+Q4wH7zNg0eL4Zm8dGOeZ8+KzW4kM0vPSEiznHmALdkar5aGHDNpngOHE7fdYGaQ5v1x2MDgAA9QBJ+W/jcQLZv7DzP/5kkAaeH/hleLvATUlg0MyWzSEC08bHi1GEi8Szecc+BZ4owbyWbWPAlpBpKH2Ywt5+CzpT/32IM3B+4k9vcffHybJ8HGgO9488Mbb/DZcoCBgQnVGcx4lINtaWBgYPxBQNEoGAWjYBSMcAAAXN9SaMbmj4EAAAAASUVORK5CYII=","orcid":"","institution":"Stanford University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Gabriella","middleName":"Barratt","lastName":"Heitmann","suffix":""},{"id":633758068,"identity":"7c67bfec-6127-46c8-8cb6-546f62078fda","order_by":1,"name":"Xue Wu","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Wu","suffix":""},{"id":633758070,"identity":"c2ae715a-109c-4c33-b95a-6d7ce3c04173","order_by":2,"name":"Anna T. 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Hsiang","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"S.","lastName":"Hsiang","suffix":""},{"id":633758093,"identity":"2343342d-2b98-4fe3-8989-748935f311b0","order_by":15,"name":"Jade Benjamin-Chung","email":"","orcid":"","institution":"Stanford University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jade","middleName":"","lastName":"Benjamin-Chung","suffix":""}],"badges":[],"createdAt":"2026-04-14 17:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9418255/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9418255/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108820233,"identity":"848f035d-d985-402d-83ee-6edd1ef9ba28","added_by":"auto","created_at":"2026-05-08 16:40:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothesized mechanisms of the impact of temperature and precipitation on \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePlasmodium vivax\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e infection.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9418255/v1/3c299f2895394777945fdd63.png"},{"id":108820539,"identity":"aaaf3a96-c8b0-42c6-a752-98cfb0174c1c","added_by":"auto","created_at":"2026-05-08 16:41:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":487121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCommunity-level \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePlasmodium vivax \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emalaria incidence within the study area. \u003c/strong\u003eDark green areas indicate forest cells from MapBiomas. Circles with black outlines represent the 136 study communities, colored by malaria incidence per 10,000 person-weeks over the study period. Community outlines represent type – squares for highway-adjacent communities and circles for riverine.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9418255/v1/113a54893ec83dbd28b0e39a.png"},{"id":108820234,"identity":"b697c5ee-a575-4ea0-bdb3-d72c61c2aaed","added_by":"auto","created_at":"2026-05-08 16:40:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":334741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeather and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePlasmodium vivax\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e malaria incidence trends over the study period. \u003c/strong\u003eAll weather variables are aggregated weekly and incidence is aggregated monthly. Shaded blue regions represent La Niña periods, while shaded yellow regions represent El Niño periods. In plot A, the red line tracks the maximum temperature observed each week, and the orange line tracks the minimum temperature observed each week.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9418255/v1/ceb418eb3d7aa47fd5cd004a.png"},{"id":108820530,"identity":"9b8ceb31-1313-4b23-a337-172952b1c42b","added_by":"auto","created_at":"2026-05-08 16:41:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":343243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePlasmodium vivax\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e malaria incidence, temperature, and precipitation over different lag periods. \u003c/strong\u003eThe associations were fit using distributed lag non-linear models. Plots A-C show the association for minimum weekly temperature, and plots D-F show the association for maximum weekly temperature, and plots G-I show the association for weekly total precipitation. Plots A, D, and G show the association between temperature and malaria incidence at the 90\u003csup\u003eth\u003c/sup\u003e percentile predictor value compared to the reference. For minimum temperature, the 90\u003csup\u003eth\u003c/sup\u003e percentile is 23.7ºC and the reference lowest minimum temperature is 20.5ºC. For maximum temperature, the 90\u003csup\u003eth\u003c/sup\u003e percentile is 33.7ºC and the reference lowest maximum temperature is 29.6ºC. For total precipitation, the 90\u003csup\u003eth\u003c/sup\u003e percentile is 1,000mm and the reference is 57 mm. Temperature models adjusted for total precipitation; total precipitation models adjusted for minimum temperature. All models were adjusted for community ID, year, years since insecticide-treated bednet delivery, and a smooth for Oceanic Niño Index. Incidence is presented per 10,000 person-weeks. Associations in plots B, E, and F were fit for the most significant lag from plots A, D, and G, respectively, using generalized additive models (GAMs). Confidence intervals were calculated using 1,000 bootstrap iterations with g-computation. GAMs do not account for temporal autocorrelation between weeks.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9418255/v1/2f75c8064aab1330c0b2ba24.png"},{"id":108820132,"identity":"40605a43-8188-4f12-8724-bf8a27982745","added_by":"auto","created_at":"2026-05-08 16:40:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":316430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePlasmodium vivax\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emalaria incidence by temperature and precipitation within El Niño-Southern Oscillation (ENSO) period and community type subgroups. \u003c/strong\u003eAll weather values represent weekly values. Associations were fit for the strongest lag identified in the distributed lag model. Community type was determined by proximity to the Iquitos-Nauta highway or a tributary to the Amazon River. ENSO periods were defined according to the Oceanic Niño Index (ONI) values for sea surface temperature during the study period. Periods with 5+ consecutive months of ONI \u0026gt;= 0.5 were defined as El Niño periods. Periods with 5+ consecutive months of ONI \u0026lt;= -0.5 were defined as La Niña periods. All other periods were considered normal periods. Associations were fit for the most significant lag identified in plot 4 using generalized additive models (GAMs). Confidence intervals were calculated using 1,000 bootstrap iterations with g-computation. GAMs do not account for temporal autocorrelations.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9418255/v1/3f8d86e13459ecb29d64e325.png"},{"id":108977492,"identity":"5748c458-7919-4731-9e42-f5cfefe26c08","added_by":"auto","created_at":"2026-05-11 11:31:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2101979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9418255/v1/e8aed980-921b-4151-a587-850249888aa1.pdf"},{"id":108820133,"identity":"61ce7447-3018-4b37-bb55-af4e517039ae","added_by":"auto","created_at":"2026-05-08 16:40:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15406,"visible":true,"origin":"","legend":"","description":"","filename":"SupplmentaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9418255/v1/6f23baa870c995fd73763a9f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between weather and Plasmodium vivax malaria in an Amazonian elimination setting: a distributed lag analysis from 2017–2024","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eAs countries approach malaria elimination, the proportion of malaria infections due to \u003cem\u003ePlasmodium vivax\u003c/em\u003e (\u003cem\u003ePv\u003c/em\u003e) increases.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e \u003cem\u003ePv\u003c/em\u003e presents unique challenges for malaria elimination efforts because of its ability to survive in wide ranging environments, including temperate settings, as a result of its dormant hypnozoite stage, which re-emerges to cause relapse infections.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e \u003cem\u003ePv\u003c/em\u003e only invades reticulocytes, early stage red blood cells, and infections are low density and often missed by standard diagnostics. Further, a high proportion of infections are asymptomatic, and thus evade detection through standard surveillance. These characteristics make \u003cem\u003ePv\u003c/em\u003e difficult to detect, treat, and ultimately eliminate.\u003c/p\u003e \u003cp\u003eThe relationship between weather and \u003cem\u003ePlasmodium falciparum\u003c/em\u003e (\u003cem\u003ePf\u003c/em\u003e) malaria transmission is well-documented, leading to routine deployment of seasonal malaria interventions, such as seasonal malaria chemoprevention\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, in places where \u003cem\u003ePf\u003c/em\u003e is endemic. However, the relationship between weather and \u003cem\u003ePv\u003c/em\u003e malaria transmission, whether seeded by relapses or new infections, is not as well understood, preventing malaria programs from considering seasonal targeting of interventions to optimize their impact. However, there is strong biologic plausibility for temperature and precipitation to influence \u003cem\u003ePv\u003c/em\u003e transmission via similar mechanisms to \u003cem\u003ePf\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Precipitation can increase standing water presence, increasing breeding ground for mosquitoes\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e; temperature has strong effects on the lifecycle of the \u003cem\u003eAnopheles\u003c/em\u003e vector, including larval development, biting rate, parasite incubation rate, and longevity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Further, there is emerging evidence that weather could directly trigger relapse \u003cem\u003ePv\u003c/em\u003e infections\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and weather may indirectly drive \u003cem\u003ePv\u003c/em\u003e infections via the saliva of uninfected mosquitoes, which contain antigens that may trigger relapse (biting rate is temperature-sensitive)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther, if seasonal patterns for \u003cem\u003ePv\u003c/em\u003e exist, strong spatial heterogeneity\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e in endemic regions requires local characterization to inform intervention targeting. Over 90% of Latin America\u0026rsquo;s malaria burden is concentrated in the Amazon Basin\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Parasite populations and vector behavior vary significantly in this region,\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e and this variation is likely driven by adaptations to different environmental conditions.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Prior studies of the relationship between weather and malaria incidence in the Amazon have not focused exlcusively on \u003cem\u003ePv\u003c/em\u003e even though \u003cem\u003ePv\u003c/em\u003e is estimated to account for 77% of the transmission burden.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Finally, the Amazon Basin is undergoing rapid environmental and climatic changes, including deforestation, warming, and drought,\u003csup\u003e24\u0026ndash;26\u003c/sup\u003e that may alter transmission dynamics at a fine scale, further emphasizing the need for local characterization.\u003c/p\u003e \u003cp\u003eOur objective was to assess the influence of rainfall and temperature on \u003cem\u003ePv\u003c/em\u003e incidence in Loreto Region, Peru, where the maximum temperatures fall between 30\u0026ndash;35\u0026ordm;C, which are above the estimated thermal optimum for malaria transmission (25\u0026ordm;C)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Daily temperature extremes are thought to limit transmission via detrimental impacts on parasite development, adult mosquito survival, egg survival, and mosquito development\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, to our knowledge, the thermal biology of \u003cem\u003ePv\u003c/em\u003e transmission by \u003cem\u003eNyssorhynchus\u003c/em\u003e (formerly \u003cem\u003eAnopheles\u003c/em\u003e) \u003cem\u003edarlingi\u003c/em\u003e, the primary vector in this region, has also not been well characterized by laboratory thermal biology studies. Information about climatic drivers of \u003cem\u003ePv\u003c/em\u003e transmission is needed to inform effective tailoring and targeting of interventions for malaria elimination in this region.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe analyzed malaria incidence data collected from January 2017 to December 2024 in all 136 communities within 8 hours of Iquitos, the periurban center of Loreto Region, Peru. This area covering 2,569 km\u003csup\u003e2\u003c/sup\u003e includes the districts of San Juan Bautista, Punchana, and Alto Nanay (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Peruvian Ministry of Health (MINSA) conducted a census in 2017 that was used to estimate the starting population size of each community. The average population size of each community was 321 individuals (range: 15 to 3223).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Loreto Region in Peru includes a large swath of the Peruvian Amazon.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Since the 1990s, rapid urbanization and deforestation in the area surrounding Iquitos, the capital of the Department of Loreto, has contributed to increased malaria burden\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, likely through increased forest edge habitat, which promotes mosquito breeding, survival, and biting\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. While this region is approaching elimination, sustained, endemic transmission remains, with an annual incidence rate of 17.4 cases per 1000 in 2019 (annual incidence\u0026thinsp;\u0026lt;\u0026thinsp;100 per 1000 is considered very low transmission\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e),\u003csup\u003e32\u003c/sup\u003e and \u003cem\u003ePv\u003c/em\u003e malaria accounts for approximately 80% of the malaria burden.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Transmission typically peaks in Loreto between February and July\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and there is a sizable burden of asymptomatic infections.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Asymptomatic infections typically go undetected and uncounted in routine malaria surveillance in Peru,\u003csup\u003e6\u003c/sup\u003e since only febrile individuals are tested.\u003c/p\u003e \u003cp\u003eFrom 2005\u0026ndash;2010, there was a large effort towards malaria elimination in Peru via the Project for Malaria Control in Andean Border Areas (PAMAFRO) program that improved case management, including through deployment of community case workers and distribution of insecticide-treated bed nets.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Transmission declined during the project, but the project did not achieve elimination, and transmission has since risen.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Following PAMAFRO, the government of Peru adopted Plan Malaria Cero (PMC) in 2017 to achieve malaria elimination by 2030. Activities conducted over the study period as part of Plan Malaria Cero include strong case management of suspected malaria cases presenting to health facilities, distribution of long-lasting insecticide-treated bed nets (ITN), and outbreak responsive activities such as active case detection (ACD), fogging, and indoor residual spraying\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome data\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was weekly community-level \u003cem\u003ePv\u003c/em\u003e malaria incidence measured in surveillance data. Data primarily included passive case detection, though ACD and RACD also occurred over the study period and were recorded in surveillance data. Both physical records stored in health posts and online records compiled by MINSA were used in this analysis. Febrile patients presented for care at a health post and were tested for malaria via microscopy.\u003c/p\u003e \u003cp\u003eAccording to PMC policy\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, blood smears are stained with 2% Giemsa for 30 minutes. Parasite densities are calculated from the number of asexual parasites per 200 leukocytes (or per 500, if\u0026thinsp;\u0026lt;\u0026thinsp;10 asexual parasites/200 leukocytes), assuming a leukocyte count of 8,000/\u0026micro;L. A blood smear is considered negative if examination of 100 high power fields does not reveal asexual parasites. Thin smears are used for parasite species identification. Slides are read by two microscopists. If there are discordant results, the results are determined by a third microscopist. Only \u003cem\u003ePv\u003c/em\u003e cases were included in the present study, and we were unable to distinguish between primary and relapse infections from microscopy data.\u003c/p\u003e \u003cp\u003eCases were matched to community census data to confirm community of residence and then matched to geocoordinates for the community centroid. Population counts per community were calculated from a government census conducted from 2017. The FocaL Mass Drug Administration for Vivax Malaria Elimination (FLAME)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e trial in Peru (NCT05690841) conducted a census on a subset of 30 communities in 2023. We estimated adjusted population counts per year by calculating the annual growth rate using the 2023 and 2017 census counts in the subset. The same flat annual growth rate was applied to all 136 communities with a population greater than 15 people in the study area.\u003c/p\u003e \u003cp\u003eWe obtained data for ITN distribution from MINSA with dates of delivery for communities included in the study from 2018\u0026ndash;2024. Time since most recent ITN delivery was discretized into never received, 0\u0026ndash;1 year, 1\u0026ndash;3 years, and more than 3 years.\u003c/p\u003e\n\u003ch3\u003eEnvironmental variables\u003c/h3\u003e\n\u003cp\u003eWe obtained temperature and precipitation data from the ERA5-Land Hourly dataset, 11 kilometer resolution, collected by the Copernicus Climate Change Service\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. We used the air temperature at 2 meters above the surface (K) band for all temperature variables and the total precipitation (m) band for precipitation variables. We matched weather variables to incidence data via community centroid.\u003c/p\u003e \u003cp\u003eThere was no missing weather data over the study period. We aggregated hourly temperature values into the weekly minimum and weekly maximum temperature; and total precipitation observed each week. All data extraction was performed using the Python API for Google Earth Engine on Google Colab servers. Aggregation over time and statistical modeling were performed in R version 4.2.1.\u003c/p\u003e \u003cp\u003eFurthermore, the Amazon region is subject to longer-term climate oscillations due to the El Ni\u0026ntilde;o Southern Oscillation (ENSO). The ENSO cycle is characterized by hot, dry conditions during El Ni\u0026ntilde;o years and rainy conditions during La Ni\u0026ntilde;a years; these periods oscillate with neutral periods every 3\u0026ndash;7 years27. We obtained data on the Oceanic Ni\u0026ntilde;o Index (ONI), a measure of the strength of the El Ni\u0026ntilde;o-Southern Oscillation (ENSO), from the National Oceanic and Atmospheric Association National Weather Service Climate Prediction Center. A 1-unit increase in ONI represents a 1\u0026ordm;C higher average sea-surface temperature compared to a 30-year reference period\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe manually designated riverine versus highway communities via Google Earth hybrid satellite image using QGIS version 3.32. We assigned communities with no land connections to the Iquitos-Nauta highway as riverine, and all other communities as highway. We checked our designations with ground-truth observations from 2023 for the FLAME community subset.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe published a pre-analysis plan at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/f89te\u003c/span\u003e\u003cspan address=\"https://osf.io/f89te\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Deviations from the pre-analysis plan are listed in Table S1. We removed years 2020 and 2021 from our primary analysis because of strong changes in incidence and case detection resulting from behavior changes and health system capacity during the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eTo assess the timing of the association between malaria incidence and weather variables, we we fit distributed lag models. We chose a 2\u0026ndash;16-week lookback period (i.e., an infection during epidemiologic week 20 looked back to weeks 4, 5, \u0026hellip;, 18) to account for variation in the potential cumulative effect of rainfall and temperature on \u003cem\u003ePv\u003c/em\u003e transmission and long-lasting impacts on relapse cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We set the upper limit at 16 weeks (4 months) to ensure we captured at least one relapse cycle in accordance with studies of tropical strains of \u003cem\u003ePv\u003c/em\u003e documenting median time to first relapse 6\u0026ndash;9 weeks after an initial infection\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and frequent relapses 3\u0026ndash;4 weeks apart for up to 4 months after the initial infection\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. We were unable to adjust for season over our study period, as data-based definitions (i.e., consecutive weeks with rolling average of precipitation above a percentile cutoff) failed to delineate clear seasons. Given prior literature states seasons generally last for 6 months\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, constraining our lookback period to 4 months also limited the effects of seasonal confounding.\u003c/p\u003e \u003cp\u003eFor weather exposures (weekly minimum temperature, weekly maximum temperature, and total precipitation), we fit non-linear distributed lag (DL) models\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e with a log link and Poisson family using weekly malaria case counts per community as the dependent variable with an offset for log community population size using the R package DLNM version 2.4.7\u003csup\u003e39\u003c/sup\u003e. For the distributed lag cross-basis function, we specified natural splines with 2 df for the exposure-response dimensions, and 1 internal lag knot to allow for differences in early versus later lag periods. This combination best balanced flexibility with stability compared to specifications with higher df values (3 or 4) and for the natural splines and knot values for the lags, which showed evidence of overfitting. All incidence ratios were calculated relative to reference weeks. In all models, we restricted weather measurements in the input data to observations between the 5th and 95th percentile of the variable\u0026rsquo;s empirical distribution to remove outlier effects. For temperature predictors, we used 20.5\u0026ordm;C for minimum temperature and 29.6\u0026ordm;C for maximum temperature as references. These values were selected because they correspond to the minimum value within the data range during the study period. For total precipitation, weeks with 57 mm of precipitation were used as the reference. Temperature variables were adjusted for the cross-basis matrix for total precipitation, and total precipitation was adjusted for the cross-basis matrix for minimum temperature, in accordance with AIC/BIC model testing. All DL models included fixed effects for year, community, and years since ITN distribution, and a smooth term for ONI with 3 knots. All positive cases were matched to weather data. Thus, we performed a complete case analysis. Because incidence data for the weather models was aggregated at the community level, we did not test for spatial autocorrelation or adjust for spatial clustering.\u003c/p\u003e \u003cp\u003eTo assess the relationship between each predictor value and malaria incidence, we fit generalized additive models (GAMs) with a Poisson family and log link and an offset for community population size. We fit models for the week with the strongest association in the DL model (primary lag). All GAMs included fixed effects for year, community, and years since ITN delivery and a smooth term for ONI with 3 knots. We derived confidence intervals from the 2.5th and 97.5th percentiles of a 1,000-replicate bootstrap distribution. For each bootstrap replicate, we used g-computation to fix the predictor at each point across its observed range and average model predictions across all observed covariate combinations.\u003c/p\u003e \u003cp\u003eWe conducted subgroup analyses using the primary lag for temperature and precipitation predictors stratifying by ENSO periods and community type. We defined periods of 5 or more consecutive months of sea surface temperature abnormality of 0.5\u0026ordm;C or above/-0.5\u0026ordm;C or below as El Ni\u0026ntilde;o/La Ni\u0026ntilde;a periods, respectively\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. To assess interaction effects between weather and ENSO period or community type on the multiplicative scale, we fit generalized linear models (GLMs) that included the same covariates from GAM models as fixed effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eDuring the study period from January 2017 to December 2024, minimum temperature varied from 17\u0026ndash;25\u0026ordm;C, and maximum temperature varied from 28\u0026ndash;41\u0026ordm;C (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Total weekly precipitation varied from 7mm per week to 2,587mm per week (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). There were 112 El Ni\u0026ntilde;o weeks, 158 La Ni\u0026ntilde;a weeks, and 147 neutral weeks over the study duration, and incidence varied by ENSO period, with the highest incidence during neutral periods. Malaria incidence was highest from 2017\u0026ndash;2019, then decreased dramatically in 2020 and 2021 and increased to levels below the 2019 levels in 2022\u0026ndash;2024. Malaria incidence did not appear to follow a distinct seasonal trend (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The mean incidence for all communities was 10.4 cases per 10,000 person-weeks.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eTemperature\u003c/h2\u003e\n \u003cp\u003eThere was a positive association between minimum temperature and malaria incidence at 2\u0026ndash;13 weeks lags, and a negative association at 14\u0026ndash;16-week lags when comparing the 90th percentile of minimum temperature (23.7\u0026ordm;C) to the 5th percentile (20.5\u0026ordm;C) (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Where this association was highest, at a 7-week lag, the association between minimum temperature and incidence was positive and nonlinear (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). At minimum temperatures of 20.5\u0026ndash;22\u0026ordm;C, incidences were similar. At higher temperatures, there was a positive, non-linear association with estimated incidence of 18 cases per 10,000 at 20.5\u0026ordm;C compared to 30 cases per 10,000 at 23.8\u0026ordm;C. Compared to weeks with the lowest minimum temperature, weeks with a minimum temperature of the 90th percentile were associated with up to 10% higher malaria incidence (95% CI 6%\u0026ndash;14%), or incidence ratio of 1.10 at a 7-week lag (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eThe association between maximum temperature and malaria incidence was non-linear and hump-shaped across lag time; there was a positive association between maximum temperature and malaria incidence at 4\u0026ndash;14-week lags when comparing the 90th percentile of maximum temperature (33.7\u0026ordm;C) to the 5th percentile (29.6\u0026ordm;C) (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Where this association was highest, at a 9-week lag, incidence increased approximately linearly from an estimated 16 cases per 10,000 to 26 cases per 10,000 as maximum temperature increased from 29.6\u0026ordm;C to 34\u0026ordm;C (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Compared to weeks with the lowest maximum temperature, weeks with a maximum temperature of the 90th percentile were associated with up to 12% higher malaria incidence (95% CI 9%\u0026ndash;15%), or incidence ratio of 1.12 at a 9-week lag (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePrecipitation\u003c/h3\u003e\n\u003cp\u003eThe association between precipitation and malaria incidence was non-linear and positive at lags of 5\u0026ndash;16 weeks, when comparing the 90th percentile of precipitation (1000mm) to the 5th percentile (57mm) (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). Where this association was highest, at an 11-week lag, incidence increased approximately linearly from an estimated 18 cases per 10,000 to 24 cases per 10,000 as precipitation increased from 57mm to 1161mm (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). At an 11-week lag, compared to weeks with no precipitation, weeks with precipitation at the 90th percentile were associated with up to 25% higher malaria incidence (95 CI 21%\u0026ndash;28%), or incidence ratio of 1.25 (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e\n\u003ch3\u003eCommunity Type Sub-group\u003c/h3\u003e\n\u003cp\u003eMalaria incidence was higher in riverine versus highway-adjacent communities (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;C). For minimum temperature, GLMs showed that associations were stronger in riverine versus highway-adjacent communities: the incidence ratio for a 1\u0026ordm;C increase in minimum temperature was 1.27 (95% CI 1.23\u0026ndash;1.31) in riverine communities compared to 1.10 (1.02\u0026ndash;1.19) in highway communities (interaction \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For maximum temperature, GLMs showed that associations were stronger in riverine versus highway-adjacent communities: the incidence ratio for a 1\u0026ordm;C increase in maximum temperature was 1.13 (95% CI 1.11\u0026ndash;1.15) in riverine communities compared to 1.03 (0.98\u0026ndash;1.09) in highway communities (interaction \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For precipitation, GLMs showed associations were weaker for riverine versus highway-adjacent communities: the incidence ratio for a 500mm increase in precipitation was 1.04 (95% CI 1.00\u0026ndash;1.08) in riverine communities compared to 1.59 (95% CI 1.44\u0026ndash;1.76) in highway communities (interaction \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasmodium vivax\u003c/strong\u003e \u003cstrong\u003emalaria incidence ratios across temperature and precipitation, by community type.\u003c/strong\u003e We fit generalized linear models (GLMs) with interaction terms to obtain incidence ratios across temperatures and precipitation values. Treating highway communities as the reference, \u003cem\u003ep\u003c/em\u003e values for interaction are shown. Temperature GLMs adjusted for precipitation at a concurrent lag, while precipitation models adjusted for minimum temperature at the concurrent lag. All GLMs were adjusted for community ID, year, Oceanic Ni\u0026ntilde;o Index, and years since insecticide-treated bednet delivery. All temperature ranges are reported in \u0026ordm;C. Precipitation ranges are reported in millimeters. Incidence ranges are reported per 10,000 person-weeks. Incidence ratios are reported per 1\u0026ordm;C increase for temperature predictors and per 500mm increase for precipitation.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCommunity type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePredictor range\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eIncidence range\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eIncidence Ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eInteraction p-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003eMinimum temperature\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHighway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e20.5\u0026ndash;23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.3\u0026ndash;13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.10 (1.02\u0026ndash;1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRiverine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e20.4\u0026ndash;23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e17.8\u0026ndash;31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.27 (1.23\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum temperature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHighway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e29.5\u0026ndash;33.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.2\u0026ndash;10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.03 (0.98\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRiverine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e29.6\u0026ndash;34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e17.4\u0026ndash;27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.13 (1.11\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecipitation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHighway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e51\u0026ndash;1139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.7\u0026ndash;16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.59 (1.44\u0026ndash;1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRiverine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e57\u0026ndash;1161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e18.3\u0026ndash;23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.04 (1.00\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eENSO Sub-group\u003c/h2\u003e\n \u003cp\u003eMalaria incidence was highest during neutral periods and lowest during El Ni\u0026ntilde;o periods (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u0026ndash;F). Using GLMs, associations between minimum temperature and malaria incidence were positive when stratifying by ENSO period and strongest during neutral and El Ni\u0026ntilde;o periods. The incidence ratio for a 1\u0026ordm;C increase in minimum temperature was 1.34 (95% CI 1.28\u0026ndash;1.39) during neutral periods, 1.28 (95% CI 1.21\u0026ndash;1.36) during El Ni\u0026ntilde;o periods (interaction \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.269), and 1.04 (95% CI 0.99\u0026ndash;1.10) during La Ni\u0026ntilde;a periods (interaction \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAssociations between maximum temperature and malaria incidence were positive when stratifying by ENSO period and strongest during neutral and El Ni\u0026ntilde;o periods. The incidence ratio for a 1\u0026ordm;C increase in maximum temperature was 1.14 (95% CI 1.11\u0026ndash;1.18) during neutral periods, 1.12 (95% CI 1.08\u0026ndash;1.16) during El Ni\u0026ntilde;o periods (interaction \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.38), and 1.06 (95% CI 1.03\u0026ndash;1.09) during La Ni\u0026ntilde;a periods (interaction \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAssociations between total precipitation and malaria incidence were positive when stratifying by ENSO period and somewhat stronger during La Ni\u0026ntilde;a periods. The incidence ratio for a 500mm increase in precipitation was 1.06 (95% CI 1.01\u0026ndash;1.12) during neutral periods, 1.07 (95% CI 0.99\u0026ndash;1.15) during El Ni\u0026ntilde;o periods (interaction \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.973), and 1.13 (95% CI 1.06\u0026ndash;1.20) during La Ni\u0026ntilde;a periods (interaction \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.183) (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasmodium vivax\u003c/strong\u003e \u003cstrong\u003emalaria incidence ratios for malaria incidence across temperature and precipitation, by ENSO (El Ni\u0026ntilde;o-Southern Oscillation) period.\u003c/strong\u003e We fit generalized linear models (GLMs) with interaction terms to obtain incidence ratios across temperature and precipitation values. Treating normal ENSO periods as the reference, \u003cem\u003ep\u003c/em\u003e values for interaction are shown. Temperature GLMs adjusted for precipitation at a concurrent lag, while precipitation models adjusted for minimum temperature at the concurrent lag. All GLMs were adjusted for community ID, year, Oceanic Ni\u0026ntilde;o Index, and years since insecticide-treated bednet delivery. All temperature predictor ranges are reported in \u0026ordm;C. Precipitation predictor ranges are reported in millimeters. Incidence ranges are reported per 10,000 person-weeks. Incidence ratios for temperature predictors are reported per 1\u0026ordm;C increase and per 500mm increase for precipitation.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSO period\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePredictor range\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eIncidence range\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eIncidence Ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eInteraction p-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003eMinimum temperature\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e19.9\u0026ndash;23.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e17.3\u0026ndash;31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.34 (1.28\u0026ndash;1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEl Ni\u0026ntilde;o\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e21.4\u0026ndash;24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.3\u0026ndash;16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.28 (1.21\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLa Ni\u0026ntilde;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e20.2\u0026ndash;23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14.2\u0026ndash;20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.04 (0.99\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum temperature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e29.5\u0026ndash;33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e20.4\u0026ndash;25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.14 (1.11\u0026ndash;1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEl Ni\u0026ntilde;o\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e30.0\u0026ndash;34.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8.5\u0026ndash;15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.12 (1.08\u0026ndash;1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLa Ni\u0026ntilde;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e29.4\u0026ndash;33.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16.4\u0026ndash;20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.06 (1.03\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecipitation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e31\u0026ndash;1104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e20.3\u0026ndash;24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.06 (1.01\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEl Ni\u0026ntilde;o\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e84\u0026ndash;1244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10.3\u0026ndash;15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.07 (0.99\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLa Ni\u0026ntilde;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e93\u0026ndash;1035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16.6\u0026ndash;19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.13 (1.06\u0026ndash;1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eHigher temperatures were associated with modestly higher \u003cem\u003ePv\u003c/em\u003e malaria incidence, and total precipitation was associated with moderately higher incidence in an elimination setting in Peru. Associations generally lasted several weeks, with more sustained, stronger associations for precipitation. We only observed associations between malaria incidence and minimum temperatures above 22\u0026ordm;C, suggesting that cooler minimum temperatures may be a limiting factor for \u003cem\u003ePv\u003c/em\u003e malaria incidence. Incidence was lowest during the dry El Ni\u0026ntilde;o periods and higher in wetter La Ni\u0026ntilde;a and neutral periods. For temperature predictors, associations were stronger during neutral and El Ni\u0026ntilde;o periods while for precipitation, associations were somewhat stronger during La Ni\u0026ntilde;a periods. Overall, periods of higher temperatures and cumulative rainfall were associated with increased malaria incidence in the subsequent several weeks.\u003c/p\u003e \u003cp\u003eOur findings suggest that weather influences malaria burden over long, 1\u0026ndash;4-month lags in this region. The length of this association likely reflects the impact of weather on vector population dynamics and biting behavior to initate multiple transmission cycles, as well as the potentially overlooked relapse periodicity of \u003cem\u003ePv\u003c/em\u003e malaria. The efficacy of standard of care anti-relapse treatment is approximately 75\u0026ndash;80%,\u003csup\u003e40\u003c/sup\u003e but effectiveness can be significantly lower due to challenges with adherence.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Further, many infections go undetected and untreated due to limited sensitivity of diagnostics, healthcare access challenges, or minimal symptoms which precludes health seeking. As such, the vast majority of \u003cem\u003ePv\u003c/em\u003e infections are estimated to be relapse infections,\u003csup\u003e43,44\u003c/sup\u003e and incidence measured in this study included both new and relapse infections. Additionally, relapses from the tropical strains of \u003cem\u003ePv\u003c/em\u003e are estimated to range anywhere from 2 weeks\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e to 2\u0026ndash;9 months,\u003csup\u003e9\u003c/sup\u003e supporting the duration of our lagged findings.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrecipitation\u003c/h2\u003e \u003cp\u003eWe found that higher precipitation was significantly associated with higher malaria incidence at lags of 5\u0026ndash;16 weeks. Our findings aligned with other studies of rainfall and \u003cem\u003ePv\u003c/em\u003e malaria focused on temperate and drier settings at similar lags. A meta-analysis in Mauritania found that \u003cem\u003ePv\u003c/em\u003e incidence was highest during and after the rainy season\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and that decreased rainfall was significantly correlated with decreased malaria burden. A study in South Korea, a temperate area, found that increased precipitation was associated with higher malaria incidence at a 10-week lag.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e A similar study in a tropical area of China also found positive associations with precipitation at lags 2\u0026ndash;4 weeks.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e A study of Amazon border regions found that \u003cem\u003ePv\u003c/em\u003e was negatively associated with precipitation at a 6-month lag\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e; however, the study did not investigate the shorter-term weather influences studied here. The lasting influence of precipitation in our findings likely reflects increased breeding ground following rainfall, which could impact multiple transmission cycles. The 5\u0026ndash;16 week lag may also include the transition from the dry season, when the vector can still thrive in moist environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTemperature\u003c/h2\u003e \u003cp\u003eWe found that higher minimum temperatures and higher maximum temperatures were both associated with higher malaria incidence from 4\u0026ndash;11-week lags and 4\u0026ndash;14-week lags, respectively. The results for minimum temperature were largely in accordance with prior literature, while the results for maximum temperature were surprising. This mixed support for our findings may reflect the substantial geographic diversity and heterogeneity of the Amazon region\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRegarding vector ecology, though the thermal transmission optimum of the \u003cem\u003ePv\u003c/em\u003e\u0026ndash;\u003cem\u003eNy. darlingi\u003c/em\u003e coupling has not been studied directly, prior studies suggest that \u003cem\u003ePf\u003c/em\u003e and \u003cem\u003ePv\u003c/em\u003e malaria transmission peaks at 25\u0026ordm;C and is limited above 30\u0026ordm;C\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. One study found the thermal optimum for malaria vectors is 25\u0026ordm;C with an upper limit of 33\u0026ordm;C,\u003csup\u003e12\u003c/sup\u003e while another found that \u003cem\u003eNy. darlingi\u003c/em\u003e adult lifespan and body size declined and development rate increased as temperatures increased from 20\u0026ordm;C to 28\u0026ordm;C. Therefore, the vector ecology literature supports our finding of positive associations with minimum temperature, which ranged from 20\u0026ndash;24\u0026ordm;C, but contrasts with our findings of positive associations with maximum temperatures, which ranged from 30\u0026ndash;34\u0026ordm;C. One possible explanation for the conflict with maximum temperature is vector biting behavior: \u003cem\u003eNy. darlingi\u003c/em\u003e typically bites humans at dusk or overnight,\u003csup\u003e52,53\u003c/sup\u003e and the highest temperatures occur in the afternoon. However, this explanation does not account for the expected declines in vector populations above 30\u0026ordm;C, driven by the negative impacts on larval development, reproduction, and adult longevity above such temperatures\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. An alternative explanation is that microclimates, topographical variation, and forest cover may moderate higher temperatures in our study site. Given that we used data with 11 kilometer resolution, our findings could not account for such fine scale phenomena. Finally, the vector in our study sites may be adapted to higher maximum temperatures\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e to prevent population decline, given strong microclimate heterogeneity\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e in the Amazon.\u003c/p\u003e \u003cp\u003eOur findings also have mixed support in the empirical \u003cem\u003ePv\u003c/em\u003e literature in similar settings. A study of Amazon border regions found that \u003cem\u003ePv\u003c/em\u003e incidence was negatively associated with minimum temperatures between 17\u0026ndash;25\u0026ordm;C at 0- and 6-month lags, in contrast to our findings for minimum temperature, but positively associated with maximum temperatures between 26.8\u0026ndash;35.2\u0026ordm;C 1- and 2-month lags\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, in accordance with our findings for maximum temperature. A study in neighboring Brazil found that mean temperatures between 25\u0026ndash;31\u0026ordm;C were associated with higher malaria risk at 1-week lag but that mean temperatures above 25\u0026ordm;C were associated with lower malaria risk\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e at 2- and 3-week lags. However, the study in Brazil used mean temperature, thereby preventing comparisons with our daily extreme measures, and both studies used different modeling structures and lookback periods, preventing direct comparisons.\u003c/p\u003e \u003cp\u003eGiven that the majority of incident \u003cem\u003ePv\u003c/em\u003e is thought to be relapse,\u003csup\u003e44\u003c/sup\u003e one additional potential explanation for the contrasting associations with temperature may be related to the unexplored relationship between environmental triggers and \u003cem\u003ePv\u003c/em\u003e relapse immunology. Though precise evidence is limited, extrinsic triggers of \u003cem\u003ePv\u003c/em\u003e relapse may include co-infection with \u003cem\u003eP. falciparum\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e or other febrile illnesses\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e that result in host inflammation, subsequent primary \u003cem\u003ePv\u003c/em\u003e infections, seasonal changes in sunlight and temperature, and mosquito bites and their associated immune responses.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e \u003cem\u003ePv\u003c/em\u003e relapse is common following a \u003cem\u003ePf\u003c/em\u003e infection\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, but \u003cem\u003ePf\u003c/em\u003e infections are uncommon in our study site. Dengue, however, is increasingly common,\u003csup\u003e57\u003c/sup\u003e and its transmission is positively associated with maximum temperatures similar to those in our study\u003csup\u003e\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e and has a higher thermal optimum (29\u0026ordm;C) and upper limit (34.5\u0026ordm;C) than malaria.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Co-infections with dengue could thus occur at higher temperatures and trigger \u003cem\u003ePv\u003c/em\u003e relapses via a similar mechanism that \u003cem\u003ePf\u003c/em\u003e infections are thought to activate. It is also possible that higher temperatures lead to host inflammation and heat stress that activate the hypnozoite.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eENSO sub-group analyses\u003c/h2\u003e \u003cp\u003eWe found that incidence was generally lowest during El Ni\u0026ntilde;o periods compared to neutral or La Ni\u0026ntilde;a periods, when incidence was similar. There is mixed support for this finding in the literature. One study in neighboring Colombia found that caseloads were higher during El Ni\u0026ntilde;o events\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, possibly because higher temperatures and lower rainfall lead to decreased river discharge and water stagnation, creating an optimal malaria breeding ground. A study in the Brazilian Amazon found that incidence was lower during El Ni\u0026ntilde;o and La Ni\u0026ntilde;a periods\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, somewhat contradicting our results. The rivers in our study region tend to be shallow and small, and some dry up completely during strong El Ni\u0026ntilde;o periods, such as in August 2023. Additionally, the Amazon has been undergoing historic drought due to global climate change\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, with 2023 being a particularly pronounced drought year. Climate change may be dramatically altering the effects of weather independently and on a longer time scale than any individual ENSO period studied here. Drought brought on by El Ni\u0026ntilde;o and climate change may decrease the availability of stagnant pools of water necessary for mosquito breeding, explaining the overall lowest incidence during El Ni\u0026ntilde;o periods.\u003c/p\u003e \u003cp\u003eAdditionally, temperatures were highest during El Ni\u0026ntilde;o periods, potentially limiting mosquito activity. On the other hand, because La Ni\u0026ntilde;a periods are associated with wetter conditions,\u003csup\u003e62\u003c/sup\u003e in the context of historic drought that has dried up rivers in our study region, increases in rainfall could potentially create temporary pools of stagnant water, explaining the higher incidence during these periods in our study region. We also found evidence of interaction between La Ni\u0026ntilde;a periods and weather predictors; associations between malaria incidence and precipitation were strongest during La Ni\u0026ntilde;a periods, while associations between malaria incidence and temperature were weakest during La Ni\u0026ntilde;a periods. These interaction effects suggest that variation in precipitation drives malaria transmission during La Ni\u0026ntilde;a periods, while during normal and El Ni\u0026ntilde;o periods, temperature largely drives transmission.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCommunity type sub-group analysis\u003c/h2\u003e \u003cp\u003eIncidence was higher in river- versus highway-adjacent communities, and the association with maximum temperature was also stronger in riverine communities. On the other hand, the association with precipitation was stronger in highway-adjacent communities. It is possible that rainfall is more likely to create stagnant pools of water in highway-adjacent communities than riverine communities, where water bodies are larger and rainfall may result in a more modest increase in ideal water bodies for mosquito breeding. Conversely, in river-adjacent communities where mosquitoes can breed uninhibited, temperature may be more limiting, explaining the stronger association with maximum temperature in these communities. In addition, higher vegetation near rivers may create cooler microclimates that approximate the malaria thermal optimum better than more urbanized highway communities, which may be hotter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur study had several limitations. We were unable to distinguish between initial and relapse infections in our incidence data, a limitation shared with prior observational studies of \u003cem\u003ePv\u003c/em\u003e\u003csup\u003e63,64\u003c/sup\u003e. We would expect that weather would influence initial and relapse cases over different lag periods, but our analysis was not able to investigate this. A follow-up study using genomic methods or a longitudinal cohort study with temporally dense testing to differentiate between initial and relapse infections could elucidate if weather differentially influences primary versus relapse infections.\u003c/p\u003e \u003cp\u003eWe were also unable to distinguish which cases were identified via passive versus active case detection, which were done as malaria control efforts by the Peruvian Ministry of Health, greatly affecting case detection and potentially creating bias in our results. However, these interventions were rare and generally done as a reaction to higher passively detected caseloads, suggesting that passive surveillance-detected incidence was already higher when active case detection was done. The study period also included two years (2021 and early 2022) where control efforts for the COVID-19 pandemic likely limited malaria transmission as well.\u003c/p\u003e \u003cp\u003eWhile we selected ERA-5 Land remote sensing data for its temporal coverage and lack of missingness, its 11 km spatial resolution prevented identification of small-scale microclimates, such as those created by forest cover and topographical features, and thus did not capture small-resolution variation that may have a large effect on mosquito breeding and survival habitat.\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e Fine-resolution drone imagery,\u003csup\u003e65\u003c/sup\u003e as used in other studies, may help overcome this limitation. While we initially considered land cover variables, they were limited in statistical power. Additionally, publicly available surface water data did not reflect our ground observations of surface water in the study site, so we did not include it in this analysis. Further research that considers the influence of non-weather environmental covariates could help elucidate the complicated, heterogeneous dynamics of malaria transmission in this region, and consider mediation under changing climatic conditions. This study was also correlational, and inferred relationships with individual weather variables may be confounded by collinearity and correlation with other weather variables and thus made it difficult to tease out individual direct effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn our study of malaria incidence in Amazonian Peru, we observed generally positive associations with higher temperatures and higher rainfall for extended lag periods beginning 4\u0026ndash;5 weeks after an initial weather event and enduring for 1\u0026ndash;4 months. We also found lower incidence during El Ni\u0026ntilde;o years and higher incidence in riverine communities, which may be used to concentrate resources and time malaria interventions to achieve elimination in this setting. Our findings indicated that malaria burden in this region may be positively associated with higher temperatures. As the successes or failures of malaria interventions may be in part due to weather initiating long-lasting \u003cem\u003ePv\u003c/em\u003e malaria cycles, these findings provide critical context to ongoing malaria elimination efforts in the Amazon region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Stanford University (72291) and by the Direcci\u0026oacute;n Universitaria de Asuntos Regulatorios de la Investigaci\u0026oacute;n de la Universidad Peruana Cayetano Heredia (211747).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll code and public data are available to replicate the analysis, figures, and tables on GitHub: https://github.com/gabbyrbh/vivax-env-rf-public. Public data are available in the 6-public-data/output/ folder. Community ID numbers have been scrambled in the public data. Data to complete the processing and weather extraction steps are private because they contain village geocoordinate and name data, but may be available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe received funding from the National Institute of Allergy and Infectious Disease, GSK, and the King Center on Global Development. Funding sources did not contribute to the study design, data collection, analysis, writing, or decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGBH: Methodology, Software, Formal Analysis, Investigation, Data Curation, Writing \u0026ndash; Original Draft, Visualization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eXW: Resources, Data Curation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eATN: Methodology, Validation, Writing \u0026ndash; Review \u0026amp; Editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAAQ: Investigation, Resources\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSRF: Investigation, Resources, Data Curation, Project administration, Writing \u0026ndash; Review \u0026amp; Editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBFC: Investigation, Resources\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAntony B: Investigation, Resources,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRC: Investigation, Resources, Data Curation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVC: Resources, Supervision\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHR: Resources, Supervision\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGCE: Investigation, Resources, Methodology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdam B: Writing \u0026ndash; Review \u0026amp; Editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eALC: Resources, Supervision, Funding Acquisition\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEAM: Writing \u0026ndash; Review \u0026amp; Editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMSH: Conceptualization, Funding Acquisition, Writing \u0026ndash; Review \u0026amp; Editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJBC: Conceptualization, Methodology, Funding Acquisition, Validation, Writing \u0026ndash; Review \u0026amp; Editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMH and JBC are Chan Zuckerberg Biohub Investigators.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKattenberg JH, Erhart A, Truong MH, \u003cem\u003eet al.\u003c/em\u003e Characterization of Plasmodium falciparum and Plasmodium vivax recent exposure in an area of significantly decreased transmission intensity in Central Vietnam. \u003cem\u003eMalaria Journal\u003c/em\u003e 2018; \u003cstrong\u003e17\u003c/strong\u003e: 180.\u003c/li\u003e\n \u003cli\u003eGlobal technical strategy for malaria 2016\u0026ndash;2030, 2021 update. 2021.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. 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Plasmodium vivax malaria incidence over time and its association with temperature and rainfall in four counties of Yunnan Province, China. \u003cem\u003eMalar J\u003c/em\u003e 2013; \u003cstrong\u003e12\u003c/strong\u003e: 452.\u003c/li\u003e\n \u003cli\u003eCarrasco-Escobar G, Manrique E, Ruiz-Cabrejos J, \u003cem\u003eet al.\u003c/em\u003e High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery. \u003cem\u003ePLoS Negl Trop Dis\u003c/em\u003e 2019; \u003cstrong\u003e13\u003c/strong\u003e: e0007105.\u003c/li\u003e\n\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":"","lastPublishedDoi":"10.21203/rs.3.rs-9418255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9418255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere is limited evidence regarding the association between weather and \u003cem\u003ePlasmodium vivax\u003c/em\u003e (\u003cem\u003ePv\u003c/em\u003e), particulary in Latin America where \u003cem\u003ePv\u003c/em\u003e is the predominant malaria species and key challenge for countries to achieve malaria elimination.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed the association between weather and \u003cem\u003ePv\u003c/em\u003e malaria incidence from 2017\u0026ndash;2024 in 136 communities in the Peruvian Amazon. Monthly community-level incidence was calculated using \u003cem\u003ePv\u003c/em\u003e case data from Notiweb, the national epidemiological surveillance system, and population census data. Predictors included weekly minimum and maximum temperature and total weekly precipitation and were calculated using hourly weather from the climate dataset ERA5. Non-linear distributed lag models were fit using a lookback period of 2\u0026ndash;16 weeks. Temperature models were adjusted for total precipitation; precipitation models were adjusted for maximum temperature. Sub-group analyses were conducted by community type (adjacent to river versus highway) and El Ni\u0026ntilde;o Southern Oscillation (ENSO) period.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMinimum temperature at the 90th percentile (23.7\u0026ordm;C) was associated with 10% (95% CI 5%\u0026ndash;14%) higher malaria incidence compared to the 5th percentile (20.5\u0026ordm;C) at a 7-week lag. Maximum temperature at the 90th percentile (33.7\u0026ordm;C) was associated with 10% (95% CI 8%\u0026ndash;13%) higher malaria incidence compared to the 5th percentile (29.6\u0026ordm;C) at a 9-week lag. Total weekly precipitation at the 90th percentile (1000mm) was associated with 29% (95% CI 24%\u0026ndash;33%) higher malaria incidence compared to weeks with the 5th percentile (57mm) at an 11-week lag. Incidence was higher and associations were stronger in communities adjacent to rivers versus highways. Malaria incidence was lower during El Ni\u0026ntilde;o periods, and there was evidence of interaction on the multiplicative scale for the association between incidence, all weather predictors, and ENSO period.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e \u003cem\u003ePv\u003c/em\u003e malaria incidence was positively associated with higher temperatures and precipitation in an elimination setting in Peru, particularly in riverine communities during non-El Ni\u0026ntilde;o years, with longer lag periods than previously reported for such associations. These findings can inform malaria elimination interventions to combat the long-lasting effects of weather on \u003cem\u003ePv\u003c/em\u003e transmission.\u003c/p\u003e","manuscriptTitle":"Associations between weather and Plasmodium vivax malaria in an Amazonian elimination setting: a distributed lag analysis from 2017–2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 16:21:16","doi":"10.21203/rs.3.rs-9418255/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-12T14:48:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192822186885831866682505989047484556514","date":"2026-05-04T00:21:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89062548970926145030313463687507274481","date":"2026-04-29T02:28:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238267077357331765274853285611549948952","date":"2026-04-27T08:48:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329478842172256135188964136463663952084","date":"2026-04-25T13:05:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289382153495337379003123574320771368957","date":"2026-04-24T11:10:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T07:34:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-23T07:00:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-23T07:00:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2026-04-14T16:59:08+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":"e6c665f0-3e05-4abf-896b-93216100ba76","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-12T14:48:12+00:00","index":41,"fulltext":""},{"type":"reviewerAgreed","content":"192822186885831866682505989047484556514","date":"2026-05-04T00:21:13+00:00","index":40,"fulltext":""},{"type":"reviewerAgreed","content":"89062548970926145030313463687507274481","date":"2026-04-29T02:28:29+00:00","index":39,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T16:21:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 16:21:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9418255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9418255","identity":"rs-9418255","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}