Malaria–climate interface in the Pacific and Andean regions of Colombia between 2014 and 2023

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The Standardized Precipitation-Evapotranspiration Index (SPEI) is used in agriculture to schedule crop planting and harvesting. The distribution and spread of malaria vectors are influenced by climatic factors, including humidity, temperature, and precipitation. The occurrence and distribution of water sources influence mosquito reproduction and transmission capacity, as well as human exposure to infectious vectors. This study evaluates the association between i) transmission of Plasmodium falciparum and vivax , represented by the time-varying effective reproductive number ( R t ), and ii) climate variables — specifically SPEI and temperature — in Colombia from 2014 to 2023. Methods Malaria surveillance data from the Colombian Public Health Surveillance System (SIVIGILA) were analyzed alongside climate data for four malaria-endemic administrative units (departments): Antioquia, Cauca, Chocó and Nariño. R t was estimated by Plasmodium species using a mechanistic framework informed by regional vector ecology and parasite dynamics. Associations between R t and climate variables, including lagged effects (1–3 months), were assessed using generalized additive models (GAMs). Results A total of 476,810 malaria cases were reported from 2014 to 2023, with 63.2% due to P. falciparum and 36.8% to P. vivax. In Chocó, generalized additive models showed a strong positive association with temperature. Together with R t , temperature explained 45.6% of the deviance for P. falciparum in Chocó (R² = 0.381, p < 0.001). In Antioquia, lower SPEI values (drier conditions) were associated with increased transmission, explaining 49.6% of the deviance for P. vivax (p < 0.01). In Nariño and Cauca, temperature was associated with P. falciparum incidence , explaining 45%-50% of the deviance (p < 0.01). R t values often rose 1–3 months prior to increases in malaria incidence. Conclusions Temperature was moderately associated with malaria transmission in the study area, particularly the Colombian Pacific Coast, specifically for P. falciparum , while both temperature and SPEI were associated with transmission of both Plasmodium species in Antioquia. These findings support integrating climate-informed surveillance indicators to enhance public health preparedness. Malaria Climate variability Effective reproduction number (Rt) SPEI Temperature Colombia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 BACKGROUND In 2023, 548,000 malaria cases were recorded in the Americas region — 72% due to Plasmodium ( P .) vivax — as were 342 deaths, with Venezuela and Brazil having the highest burden ( 1 ). In 2023, Colombia had the highest number of P . falciparum cases , with 37,851 (35.9%) ( 1 ). Of these, 1,713 (1.6%) were severe malaria cases, and the departments (highest administrative divisions) with the highest incidence were Chocó, Antioquia, Nariño, Córdoba and Bolívar ( 1 , 2 ). Approximately 66.6% of Colombian territory is within 1,600 meters of sea level. This area encompasses around 750 municipalities (second-level administrative units) and is characterized by warm temperatures (17°C to 34°C), high relative humidity (up to 90%), and the influence of cyclical climatic phenomena such as El Niño and La Niña. These climatic and topographic conditions create a dynamic setting for malaria transmission, shaped by interacting environmental, biological, and social factors. We distinguished between two major transmission regions: the Andean region (Antioquia), characterized by temperatures slightly lower than other zones such as the Pacific and Atlantic coast( 3 – 5 ) and shorter transmission seasons, and the Pacific region (Chocó, Cauca and Nariño), known for its perennial transmission, warmer climate, and higher entomological inoculation rates ( 4 , 5 ). These areas include tropical rainforests, mangroves, and lowland river systems, which provide optimal habitats for malaria vectors such as Anopheles albimanus and Anopheles nuneztovari . These ecological conditions support sustained malaria transmission year-round​ ( 6 ). Demographic factors also have an important role in the chain of transmission. Indigenous settlements and reservations are in areas of high epidemiological risk ( 6 ). There is substantial migration, especially across the border with Venezuela and at the Darien Gap, the latter headed to Panama and onwards to North America. Departments on the Pacific coast have a large Afro-Colombian population, often with Duffy blood group, which protects against P. vivax infection ( 7 ). Situations of public order forced displacement, socioeconomic inequalities — such as extreme poverty, lack of basic sanitation, education, work in illegal mining or crops — as well as medication shortages have increased the persistence and spread of malaria ( 8 , 9 ). The reproduction and survival of the Anopheles vectors are strongly dependent on climatic variables such as temperature and humidity ( 10 , 11 ). Temperature also affects the parasite's extrinsic cycle ( 12 ). In this context, epidemiological indicators such as the time-varying effective reproductive number ( R t ) are particularly valuable. R t incorporates both the extrinsic incubation period (parasite development within the mosquito) and the intrinsic period (parasite development within the human host), and is closely linked to vector life conditions, reproduction capacity, and the susceptibility of the human population (( 13 – 15 ). R t estimates the average number of secondary cases generated by an infected individual during their infectious period, and how a pathogen spreads within a specific population, based on factors such as the pathogen's virulence, immunity (susceptibility), population density, and interactions among people ( 16 , 17 ). This dynamic indicator provides a more accurate measure of transmission potential, enabling the identification of critical periods and the emergence of epidemic outbreaks ( 18 ). Its responsiveness to temporal changes in transmission makes it a tool for monitoring and anticipating shifts in malaria dynamics under varying climatic conditions. Vector-borne diseases such as malaria are highly sensitive to environmental fluctuations, including changes in temperature, rainfall and moisture. While temperature is known to accelerate parasite development and influence vector survival ( 5 ), the effects of water balance are not fully captured by precipitation alone ( 19 ). The Standardized Precipitation-Evapotranspiration Index (SPEI) integrates both precipitation and atmospheric evaporative demand and serves as a more comprehensive proxy for drought intensity and moisture availability — critical factors in the formation and persistence of mosquito breeding habitats ( 20 ). Drought conditions, as measured by SPEI, have been associated with elevated risks of vector-borne diseases in previous studies, particularly through mechanisms such as the concentration of breeding sites, shifts in vector density, and increased human–vector contact around scarce water sources ( 19 , 21 ). Although SPEI remains underutilized in malaria research, it has shown value in agricultural and hydrological forecasting, offering insights into climatic conditions that may influence malaria transmission dynamics in endemic regions( 21 , 22 ) Climatic variability can generate periods of high transmission, followed by abrupt drops in incidence, posing challenges for predicting and managing the disease (( 22 ). The use of joint epidemiological and climatic models ( 23 ) has the potential to facilitate not only the retrospective analysis of malaria trajectories, but also early warning measures based on indicators such as R t , temperature, and less well-known indices such as SPEI ( 19 , 20 , 24 ) For high-incidence regions or endemic areas of Colombia, the present study analyzes the association between malaria R t and climatic variation in terms of SPEI and temperature. The integration of climatic and epidemiological data facilitates the identification of risk patterns or seasonal trends, potentially guiding local control strategies. METHODS This study employed an ecological (i.e., area-level) time-series design to assess the association between climatic variability and malaria transmission dynamics across parasite species in Colombia between 2014 and 2023. Specifically, we examined the relationship between the time-varying effective reproduction number ( R t ) for malaria ( P. vivax and P. falciparum) and climatic indicators: in particular, the Standardized Precipitation-Evapotranspiration Index (SPEI) and temperature. The analysis focused on four administrative units: the departments of Antioquia, Chocó, Cauca and Nariño. These locations were selected for their status as endemic regions or for observed shifts in malaria incidence over the study period. In Cauca and Nariño, only P. falciparum was analyzed, because this species accounted for 90% to 96% of the total number of cases over the previous ten years. The design allowed for temporal and spatial comparisons to identify climate-driven transmission patterns in both Pacific and Andean ecological contexts. Data sources Data were obtained from the national Public Health Surveillance System (SIVIGILA), the AgERA5 dataset (Copernicus Climate Change Service), and population estimates from the National Administrative Department of Statistics (DANE). From SIVIGILA, we obtained individual-level data on malaria cases reported via routine surveillance, confirmed by Rapid Diagnostic Test (RDT) or thick blood smear in accordance with the case definition of the Colombian INS (National Institute of Health). SPEI is the Z score of the difference between precipitation and reference evapotranspiration ( ET 0 ). ET 0 (in mm per day) was estimated using the Priestley-Taylor Eq. (25) due to its simplicity( 26 ) its acceptable performance in low latitudes ( 26 ), and its minimum input requirements ( 18 ): $$\:{ET}_{0}=a\frac{\varDelta\:}{\varDelta\:+\gamma\:}\frac{{R}_{n}}{\lambda\:}$$ where α = 1.26 is the dimensionless Priestley-Taylor evaporative coefficient, and λ = 2.45 MJ/kg is the latent heat of vaporization at 20°C. Values of ∆ (slope of the vapor pressure-temperature curve), γ (psychrometric constant for different altitudes), and R n (net radiation, as opposed to the R t reproduction number) were calculated following the FAO-56 procedure ( 28 ). To obtain the SPEI, a logistic curve is fitted to observed differences between precipitation and ET 0 , and the resulting cumulative distribution function (CDF) is converted to a Z score via the CDF of a standard normal (Gaussian) distribution ( 29 ). Precipitation, radiation, and altitude data for calculating the SPEI were obtained from satellite-based raster images in the AgERA5 dataset. This integrates daily meteorological station data to support agronomic applications and agricultural models. The information is provided in grid cells at a spatial resolution of 0.05° (approximately 10 km), corresponding to hourly ERA5 data—the fifth-generation atmospheric reanalysis produced by the ECMWF (European Centre for Medium-Range Weather Forecasts)( 20 , 30 , 31 ) Descriptive analysis and spatial visualization A descriptive analysis characterised malaria transmission patterns across departments, parasite species and time. Monthly and annual malaria case counts were summarised by department and Plasmodium species, and incidence rates were calculated using department-specific population estimates. Sociodemographic details of reported cases, including age, sex, area of residence, ethnicity, insurance status, pregnancy status and clinical outcomes, were evaluated using medians and interquartile ranges for continuous variables and frequencies and percentages for categorical data. Differences among departments were assessed using the Kruskal–Wallis test for continuous variables and Pearson’s chi-squared test for categorical variables. To examine temporal and spatial variations in malaria transmission, time-series plots of malaria incidence and the effective reproduction number ( R t ) were created. Additionally, spatial heat maps of annual malaria incidence rates were produced to display geographic patterns and the persistence of high-transmission areas over the study period. Endemic channel To identify epidemic episodes, contextualize malaria incidence, including fluctuations, and find unusual transmission levels, endemic channels (epidemic thresholds) were constructed for each department. These channels enable the classification of observed malaria case counts into ranges representing expected and excessive transmission, relative to historical data trends. Outbreak or epidemic status is defined by the difference between the observed and expected numbers of cases over a specific period, area (department), and population. More specifically, the methodology of Bortman ( 32 ) was used to define zones of safety, alert, alarm, and epidemic based on measures of central tendency and dispersion. The channels were calculated using the epiCo R package (version 1.0.1) from the epiverse-trace project, to derive the endemic threshold from monthly malaria case data aggregated by department. Specifically, we applied a 5-year baseline window, excluding the epidemic year under evaluation. Observed monthly case counts were overlaid and assessed as being in the safety zone (below 75th percentile), alert zone (75th–90th percentile), or alarm zone (above 90th percentile). Serial interval and time-varying effective reproduction number R t The serial interval (SI) is defined as the time between the symptom onset in a primary malaria case and the symptom onset in a secondary case directly infected by the former. For a pair of individuals, j infected earlier and i later, with symptom onset times t j and t i , the SI is t j - t i . The probability distribution of the serial interval links symptom timing and transmission likelihood ( 23 ). In vector-borne infections such as malaria, the SI does not arise from a single biological process but instead is the sum of different times, including (a) the time between symptom onset and the period of infectiousness to mosquitoes (gametocyte emergence), the liver exo-erythrocytic phase LEP, (b) the time required for mosquitoes to become infectious after acquiring parasites, that is considered as human to mosquito transmission period, HMTP, (c) the period after to susceptible mosquito acquire the parasite from the human and duration of mosquito infectivity (constrained by lifespan) EIP, (d) the incubation period in the newly infected human host prior to symptom onset, MHTP and finally (e) the interval between infection to detection by either way such as clinic assessment or diagnosis test, IDP ( 13 , 23 , 33 ). The model parameters were chosen to reflect the regional dynamics of malaria transmission in Colombia, by department and parasite species, using a simulation-based mechanistic framework. This model incorporated ecological assumptions regarding the dominant vector species in each department, which influence key transmission parameters such as mosquito daily survival, EIP, and biting rate. Specifically, Anopheles albimanus was assumed to be the predominant vector in Chocó and Nariño, in Cauca, An. nuneztovari , it was considered based on vector distribution information in Colombia. For Antioquia, particularly in its Urabá region, a model was developed. darlingi , given their documented presence in this region( 5 , 34 – 36 ). The mosquito survival probabilities, biting rates, and EIP durations were chosen accordingly in the simulation model for each department, resulting in differentiated serial interval distributions that reflect local eco-epidemiological conditions. Each component of the SI was parameterized based on mechanistic evidence. The liver exo-erythrocytic phase (LEP) for P. vivax was assumed to be ~ 8.5 days at 26°C ( 5 ). The human infectious period (HMTP) was modelled using Gaussian infectiousness curves, reflecting earlier and longer gametocyte carriage in P. vivax than in P. falciparum ( 13 ). For EIP, a normal distribution was used, with region-specific means and standard deviations, drawing on entomological studies indicating temperature-dependent development rates. For instance, P. vivax EIP was modelled with mean durations of 12 days (Pacific) and 20 days (Andean), aligned with published thermal performance models ( 5 , 14 ), and the mosquito lifespan was estimated using a geometric distribution based on daily survival probabilities specific to vector species and climate. Finally, treatment delays were simulated using Poisson distributions, assuming shorter delays in the IPD in Antioquia (2.5 days compared with 3.5 days in other departments) due to greater health service accessibility ( 9 ). This framework was used to simulate 10,000 full transmission stages and cycles for each combination of region and parasite, representing a sequence through infection and symptom development (Additional file Table S1 and Figure S1 ). The estimated SI distributions for each parasite species and region were incorporated into the estimation of the time-varying effective reproduction number ( R t ) using the EpiEstim R package ( 16 ). Values above 1 indicate increasing transmission, while values below 1 indicate transmission decline ( 15 , 18 ). Temperature Daily maximum temperature ( T max ) values from meteorological and satellite stations were obtained from the AgERA5 dataset across the full extent of each department. The data are available in ECMWF format for SPEI. For SPEI, the rasters were clipped to each department's vector layer to extract the average T max . As for other variables, the temperature data of the pixels in each grid were weighted according to the proportion of the area that is within the selected. Monthly averages were also calculated. Effect of climate (SPEI and temperature) on transmission (Rt) For the malaria case series, the exploratory analysis excluded incomplete or inconsistent records, and the data were evaluated to ensure temporal continuity of the monthly and annual series. Then, descriptive analysis of the sociodemographic characteristics of malaria cases was used to characterize the epidemiology of the disease. For R t , SPEI, and temperature, the exploratory analysis included identifying seasonality and calculating partial autocorrelation (PACF) and autocorrelation (ACF). Lags for the three variables at 1, 2, and 3 months were selected as candidate variables for evaluating the effects of climate on R t . Generalized additive models (GAMs) ( 38 ) were used to assess the association between climate variables and malaria transmission in each department. This model incorporates smoothing terms, facilitating the modelling of complex patterns in epidemiological data on incidence, transmission, and climate ( 39 ), and handling lagged effects. Additionally, multiple GAM specifications were assessed by varying the smoothing function types and the lag terms (1-, 2-, and 3-month). For each GAM specification, the deviance explained was calculated to determine whether the patterns observed in the fitted smooths could arise by chance. In addition, the stability of the estimated smooth terms (i.e., temperature and SPEI) and coefficients (i.e., consistency of effect sizes across model specifications) Model performance was assessed using the percentage of deviance explained. The final model specification incorporated tensor product smooths ( te ) to flexibly model the joint effects of climatic variables and their lags (e.g., SPEI and temperature). Within the tensor product, smooth, cubic splines were used. A separate model was used for each combination of Plasmodium species and department. This approach was chosen over univariate smooths due to its ability to handle predictors on different scales and its suitability for modelling interactions. Additionally, to adjust for the observed autocorrelation in malaria transmission, the reproductive number at a one-month lag was included as a parametric term. Finally, models were compared using AIC for each department and parasite species evaluated, selecting the best model based on the highest deviance explained and biological plausibility (consistent with mosquito and parasite life cycles). All statistical analyses were conducted using R (version 4.3.2). RESULTS Characteristics of malaria cases Between the start of 2014 and the end of 2023, 476,810 cases were reported to SIVIGILA from the study area (four departments). Of these, 301,427 (63.2%) were due to P . falciparum and 175,383 (36.8%) to P . vivax . Just over half (53.5%) of the cases were from the department of Chocó, and almost another quarter (23.6%) were from Nariño. The overall annual incidence rate was 5,398 per 100,000 inhabitants in the whole study area. Figure 3 maps the incidence for each year. Table 1 Cases of malaria by department and Plasmodium species, 2014–2023. Department P. vivax P. falciparum Total Antioquia 62,582 (79.2%) 16,413 (20.8%) 78,995 Cauca 552 (2.4%) 22,821 (97.6%) 23,373 Chocó 100,764 (39.5%) 154,118 (60.5%) 254,882 Nariño 11,485 (9.6%) 108,075 (90.4%) 119,560 Total 175,383 (36.8%) 301,427 (63.2%) 476,810 Among the four departments, Chocó experienced major outbreaks by P. falciparum in 2015 and early 2016, with monthly cases surpassing the alarm threshold of 1,200 cases (Fig. 1 ). P. vivax showed similar patterns but with lower incidence. From 2019 to 2021, transmission in Chocó was relatively stable, before a sharp post-pandemic increase in 2022 and a more pronounced outbreak in late 2023, during which the incidence of P. falciparum and P. vivax became comparable. In Nariño, incidence steadily increased from 2015, rising from 110 to over 250 monthly cases between 2018 and 2020, with recurrent short- to medium-duration outbreaks. Case counts decreased significantly in 2021–2022 (returning to the safety zone) but rebounded into the alert zone in 2023. Cauca exhibited lower overall malaria transmission but showed a consistent pattern of moderate P. falciparum outbreaks during 2015–2016, followed by a sharp, isolated peak in mid-2019. Afterwards, incidence remained below endemic thresholds until a resurgence in late 2023. In Antioquia, transmission remained largely within endemic levels for both species, although both occasionally exceeded the alert level. Across departments, the median age ranged from 19 to 26 years (Table 2 ). Most cases were male (60%), of African descent (51%), and rural (76%). A total of 146 deaths were reported over the 10 years (case fatality of less than 0.1%), with 96 (65.7%) of those deaths in Chocó. Table 2 Sociodemographic characteristic malaria cases in 2014–2023 Characteristic ANTIOQUIA N = 78,995 1 CAUCA N = 23,373 1 CHOCO N = 254,882 1 NARIÑO N = 119,560 1 Overall N = 476,810 1 p-value 2 Age 22 ( 13 , 34 ) 26 ( 15 , 39 ) 19 ( 9 , 34 ) 23 ( 14 , 34 ) 21 ( 11 , 34 ) < 0.001 Sex < 0.001 F 29,840 (38%) 10,159 (43%) 113,607 (45%) 50,167 (42%) 203,773 (43%) M 49,155 (62%) 13,214 (57%) 141,275 (55%) 69,393 (58%) 273,037 (57%) Area < 0.001 Rural 72,588 (92%) 18,576 (79%) 163,868 (64%) 90,723 (76%) 345,755 (73%) Urbana 6,407 (8.1%) 4,797 (21%) 91,014 (36%) 28,837 (24%) 131,055 (27%) Ethnicity < 0.001 Afro-descendant 8,134 (10%) 21,727 (93%) 155,004 (61%) 108,053 (90%) 292,918 (61%) Indigenous 13,217 (17%) 551 (2.4%) 81,711 (32%) 2,696 (2.3%) 98,175 (21%) Mixed 57,413 (73%) 1,055 (4.5%) 17,434 (6.8%) 8,597 (7.2%) 84,499 (18%) Raizel 90 (0.1%) 17 (< 0.1%) 226 (< 0.1%) 67 (< 0.1%) 400 (< 0.1%) Rom / Gypsy 141 (0.2%) 23 (< 0.1%) 507 (0.2%) 147 (0.1%) 818 (0.2%) Insurance type < 0.001 Contributive 5,820 (7.4%) 2,449 (10%) 14,702 (5.8%) 7,082 (5.9%) 30,053 (6.3%) Exception 1,617 (2.0%) 408 (1.7%) 4,753 (1.9%) 2,881 (2.4%) 9,659 (2.0%) Not insured 12,275 (16%) 3,494 (15%) 34,570 (14%) 17,859 (15%) 68,198 (14%) Special 384 (0.5%) 66 (0.3%) 1,937 (0.8%) 1,152 (1.0%) 3,539 (0.7%) Subsidize 58,899 (75%) 16,956 (73%) 198,920 (78%) 90,586 (76%) 365,361 (77%) Pregnant 618 (0.8%) 144 (0.6%) 1,446 (0.6%) 1,224 (1.0%) 3,432 (0.7%) < 0.001 Plasmodium vivax 62,582 (79%) 552 (2.4%) 100,764 (40%) 11,485 (9.6%) 175,383 (37%) falciparum 16,413 (21%) 22,821 (98%) 154,118 (60%) 108,075 (90%) 301,427 (63%) Final condition Alive 78,979 (100%) 23,369 (100%) 254,815 (100%) 119,548 (100%) 476,711 (100%) 1 Median (Q1, Q3); n (%) 2 Kruskal-Wallis rank sum test; Pearson's Chi-squared test Time-varying effective reproduction number ( R t ) In Chocó, particularly for P. falciparum , the number of cases spiked sharply during the major outbreak of early 2016, with R t exceeding 1.5 (95% CI: 1.45–1.65). From late 2022 through 2023, R t showed a renewed upward trend. P. vivax in Chocó displayed short-lived peaks generally between 1.5 and 2.0 (95% CI: 1.61–2.08). A notable increase was recorded in 2019, when transmission of both parasite species rose substantially. In Nariño, P. falciparum transmission showed multiple intermittent increases, most prominently between 2018 and 2020, with peaks approaching R t = 1.97 (95% CI: 1.78–2.17). These increases typically preceded corresponding increases in reported cases by 1 to 3 months. Cauca experienced the most pronounced rise in P. falciparum R t in 2015, reaching approximately 3.5 (95% CI: 2.60–4.50). However, case numbers during this period remained relatively low and stable. In 2020, R t again rose above 2.5 (95% CI: 2.04–3.07), coinciding with the largest recorded case peak. In Antioquia, P. falciparum exhibited marked fluctuations in transmission intensity throughout the evaluation period, with the highest R t recorded in May 2020 at 2.21 (95% CI: 1.74–2.78). Although P. falciparum case numbers were substantially lower than those of P. vivax , the latter species maintained values close to 1.0 for most of the period, except in May 2020, when R t rose to 1.96 (95% CI: 1.70–2.30). Patterns of SPEI and temperature SPEI exhibited seasonal fluctuations across departments (Fig. 4 ), while malaria incidence fluctuated in parallel but with lags in some departments. In Chocó, P. falciparum incidence peaked in 2016, 2018, and late 2022–2023, and tended to occur during or shortly after phases of moderately negative SPEI, i.e., drier–than–normal conditions. For P. vivax in Chocó, peaks in incidence were smaller in magnitude but still clustered around SPEI downturns, particularly during 2019–2020. In Antioquia, P. falciparum transmission displayed multiple moderate peaks that often followed short-term SPEI declines, whereas P. vivax exhibited more pronounced peaks in 2018 and 2023, each preceded by a gradual shift to negative values, indicating a potential climate signal with a lagged effect of several months. Cauca presented less consistent relationships. The sharpest P. falciparum increase in 2020 coincided with a phase of mildly lower SPEI, but other peaks occurred under neutral SPEI, suggesting that local non-climatic factors played a stronger role here. In Nariño, P. falciparum transmission between 2018 and 2020 rose during a sustained period of negative SPEI, while the subsequent decline in cases (2021–2023) coincided with a return to near-neutral or positive SPEI conditions. These results indicate that malaria peaks tended to be more likely during or following periods of negative SPEI, particularly in Chocó and Antioquia, and often with a lead time of one to three months between climate signal onset and epidemiological response. However, the strength and consistency of this relationship varied by department. The relationship between temperature and malaria incidence (Fig. 5 ) showed notable heterogeneity among departments. In Chocó, both P. falciparum and P. vivax displayed major incidence peaks in early 2016, late 2019–2020, and 2023. These peaks occurred during periods of relatively high temperatures (25–27°C). By Antioquia, temperatures remained comparatively stable (21–23°C) throughout the study period, yet moderate P. vivax peaks in 2018 and 2023, and P. falciparum increases in 2020, coincided with the warmer end of this range, suggesting that small seasonal variations can be sufficient to influence the dynamics of the cases. For Cauca, P. falciparum incidence peaks—particularly in 2020— were not preceded by marked temperature anomalies, suggesting that climatic variables played a limited role in driving transmission during that period. A similar pattern was observed in Nariño, where P. falciparum transmission between 2015 and 2019 coincided with moderately warmer years (22–23°C), but the notable decline in cases from 2020 onward occurred despite persistently similar temperature conditions, pointing to non-climatic drivers as the dominant influence during the later period. These findings suggest that, while temperature appears to play a consistent and amplifying role in Chocó and, to a lesser degree, in Antioquia and Nariño, its association with malaria transmission is less evident in Cauca. The influence of temperature also appears to be modulated by local ecological and environmental contexts, suggesting that its effect on malaria risk may not be uniform across regions. Evaluating associations using a generalized additive model. Generalized additive models (GAMs) with lagged climate predictors revealed heterogeneous associations between climatic variables and malaria transmission across departments and Plasmodium species. The best-fit models incorporated 2-month lags for temperature and SPEI in Chocó, Antioquia, and Nariño, and 3-month lags in Cauca, along with a 1-month lag for R t to account for short-term persistence in transmission In Chocó, temperature showed a strong and statistically significant relationship with R t for both P. falciparum ( F = 3.28, p < 0.001) and P. vivax ( F = 3.27, p < 0.001), explaining 45.6% of the deviance. Si Effect plots indicated a non-linear response, with lower R t values around 24.5°C and increases at both lower ( 26°C) temperatures. SPEI, lagged by two months, was not statistically significant (p > 0.05) but showed a slight negative trend for P. vivax under moister conditions. In Antioquia, SPEI significantly influenced R t for both P. falciparum ( F = 4.03, p < 0.001) and P. vivax ( F = 2.44, p = 0.002), although the explained variance was lower ( R ² = 0.362, deviance = 49.6%). Temperature effects were significant for P. falciparum ( F = 1.97, p = 0.008) but not for P. vivax (p = 0.475). The temperature and R t curve for P. falciparum showed a sharp decline above ~ 21.5°C followed by a plateau. In Cauca, SPEI was not significantly associated with Rt (p = 0.189), but temperature had a strong effect on P. falciparum Rt (F = 3.34, p = 0.004; R² = 0.385, deviance = 50.8%). The temperature response curve showed increased transmission above 22.8°C, peaking near 23°C. The longer, 3-month lag suggests that, in this cooler setting, it can influence vector and parasite dynamics. In Nariño, SPEI did not significantly affect R t (p = 0.122), whereas temperature was strongly and positively associated with P. falciparum , R t ( F = 3.60, p < 0.001; R² = 0.371, deviance = 45.9%). The temperature effect plot showed a steady increase in Rt with increasing mean temperature, peaking at ~ 22.8°C. The 2-month lag here reflects a similar climate–transmission delay to that in Chocó and Antioquia, despite Nariño’s greater geographic and climatic heterogeneity. Finally, the temperature emerged as the dominant climatic driver of R t variability in most departments, particularly for P. falciparum . SPEI effects were more variable, with stronger signals in Antioquia and subtle trends in Chocó. Warming or lower values in the SPEI (lesser moisture) precede increases in Rt by approximately two to three months, providing a potential early-warning window before case surges occur. Table 3 Performance of GAMs for Climate Effects on Malaria R t by department Department Variable Parasite F p R 2 Percent deviance explained Chocó SPEI P. falciparum 1.763 0.106 0.381 45.6% SPEI P. vivax 1.190 0.303 Temperature P. falciparum 3.282 < 0.001* Temperature P. vivax 3.271 < 0.001* Antioquia SPEI P. falciparum 4.028 < 0.001* 0.362 49.6% SPEI P. vivax 2.443 0.002* Temperature P. falciparum 1.972 0.008* Temperature P. vivax 0.837 0.475 Cauca SPEI P. falciparum 0.162 0.189 0.385 50.8% Temperature P. falciparum 3.343 0.004* Nariño SPEI P. falciparum 1.977 0.122 0.371 45.9% Temperature P. falciparum 3.603 < 0.001* Performance metrics of three generalized additive model (GAM) specifications applied separately for each department. For each model, the table provides the adjusted R ², the percentage deviance explained, and the p-value for the smooth terms relative to the null model. * p < 0.05. The temperature presented in the table corresponds to the maximum temperature for each entity DISCUSSION Our study underscores the significant role of climate variability, especially temperature fluctuations and moisture availability conditions measured by the Standardized Precipitation and Evapotranspiration Index (SPEI), with malaria transmission dynamics, as reflected in the effective reproduction number ( R t ). These findings align with previous global and regional studies, reinforcing the link between climate variability and vector-borne disease dynamics ( 10 , 37 ) In addition, our findings also show that SPEI can be relevant in certain regions for explaining malaria transmission. ( 3 , 10 , 40 , 41 ) Unlike other climatic measures related to rainfall or drought, SPEI integrates both precipitation and potential evapotranspiration, thus capturing net water availability in the environment. This is important because mosquito larval development depends not only on rainfall inputs but also on the persistence of standing water. Positive SPEI values (moisture-average conditions) can sustain breeding habitats for longer periods, enhancing vector survival and reproduction, while negative SPEI values (drier conditions) may accelerate habitat drying and reduce larval survival. However, in some cases, moderate negative anomalies can also concentrate breeding in more permanent water bodies, potentially increasing vector–host contact rates ( 5 , 11 , 35 ). Low SPEI values—indicating drier conditions—were associated with higher R t in some settings, particularly Chocó( 11 , 34 ). Similar dynamics have been reported in sub-Saharan Africa ( 38 ), where drought conditions increased malaria transmission risk, and large water reservoirs created localised, stable breeding habitats in otherwise drier landscapes, leading to increased vector densities and transmission risk in nearby communities. In Anopheles albimanus , the primary malaria vector in Chocó, larvae have been documented in a wide variety of permanent and human-made water bodies, including fishponds, lagoons, ditches, excavation sites, and puddles, even during dry spells and when water is marginal in quality( 39 ). This ecological tolerance allows vector populations to persist in fewer but stable aquatic habitats—such as shaded excavations or domestic wash sites—when rainfall is scarce. In low-SPEI scenarios, when temporary breeding locations disappear, these remaining bodies of stagnant water may act as concentrated ‘hotspots’ for larval development, maintaining transmission potential in otherwise challenging conditions ( 35 , 36 , 40 ). Temperature is a key driver of malaria transmission, affecting both vector populations and parasite development. Warmer temperatures accelerate the extrinsic incubation of Plasmodium parasites, reducing the time required for mosquitoes to become infectious ( 41 ). However, the relationship between temperature and malaria transmission is not linear, and our study found important regional differences. Across all departments, temperature consistently predicted malaria transmission, with warmer conditions generally associated with higher R t values. In Chocó, for both P. falciparum and P. vivax , we observed a strong positive relationship between temperature and R t , suggesting that higher temperatures likely intensified transmission. This finding is consistent with previous studies indicating that the biology of An. albimanus , which thrives in sunlit, stagnant water bodies and is highly efficient in coastal floodplain environments, and An. darlingi , which is also present and well-adapted to rainforest ecosystems. Optimal transmission conditions for these species have been observed at temperatures between 20–30°C, particularly around 26°C( 4 , 5 , 10 , 35 ). In Antioquia, a similarly strong temperature– R t association was observed, where An. nuneztovari and An. darlingi are the primary vectors. These species prefer humid forest and foothill habitats, with more stable microclimates in the 22–29°C range and may be more sensitive to temperature fluctuations due to their breeding and resting behaviour ( 4 , 24 , 35 ). These findings suggest that vector-specific ecological preferences modulate the strength and shape of the temperature–transmission relationship, an aspect that can merits further investigation in Colombia’s diverse eco-epidemiological settings ( 4 , 42 ). An important finding of our study was that R t often peaked between one to three months before an increase in reported malaria cases; these findings align with previous studies showing that climatic factors have effects on malaria( 23 , 43 ). From a biological perspective, the lag intervals correspond to the expected delays between climate anomalies and their downstream impact on malaria transmission, considering the extrinsic incubation period (EIP) of the parasite in the mosquito (typically 10–20 days), mosquito population response time to environmental changes (1–3 weeks), and human intrinsic incubation period (10–15 days), leading to an anticipated lag of 1 to 3 months between climate changes and observable shifts in transmission ( R t ). However, regional variability in model performance emphasises the need for local calibration and suggests that non-climatic drivers, such as vector control, land use, or socio-demographic shifts, may modulate climate impacts in certain settings. Several limitations of our study should be acknowledged. The use of surveillance data from SIVIGILA introduces potential biases. Underreporting is a common challenge in malaria surveillance, and reporting completeness may vary by region and altitude. If malaria cases are more frequently reported in cooler, high-altitude areas, this could bias estimates of the relationship between temperature and malaria. Also, the entire department was used to extract temperature data, but malaria is not necessarily endemic throughout the department. Additionally, non-climatic factors, such as land-use changes, migration, and healthcare access, likely modulate malaria transmission and should be incorporated into future models. Likewise, finer-scale vector data are needed to improve the predictive accuracy of climate-based models. Lastly, further studies should explore potential threshold effects, identifying specific temperature and SPEI cutoffs that significantly alter malaria risk. CONCLUSION Our findings highlight the role of climate variability, particularly temperature fluctuations and the SPEI balance, in shaping malaria transmission dynamics in Colombia. Temperature was consistently associated with increased transmission potential, while lower moisture conditions, as reflected by lower SPEI values, were also linked to higher R t . These patterns suggest that climatic factors can intensify malaria risk by both enhancing vector survival and altering human–mosquito contact. The study underscores the importance of incorporating climate indicators into malaria surveillance systems to support early warning and targeted control strategies. A major implication of our study is the potential use of R t as an early warning indicator for changes in malaria transmission. Although we observed that R t often increased prior to rises in reported cases, further research is needed to formally assess lead times and predictive performance. Our findings suggest that combining R t with climate indicators such as temperature and SPEI may offer valuable insights into transmission dynamics, but further evaluation is required before these tools can be integrated into operational surveillance or early warning systems. Despite these insights, malaria transmission remains a multifactorial process influenced by non-climatic variables such as land use, human migration, and healthcare accessibility. Future research should incorporate these factors to refine predictive models. As climate change continues to alter malaria risk patterns, proactive, data-driven interventions will be essential to sustain progress toward control and malaria elimination in Colombia. Abbreviations ACF Autocorrelation function AIC Akaike Information Criterion CDF Cumulative distribution function CI Confidence interval CIDEIM Centro Internacional de Entrenamiento e Investigaciones Médicas CIAT International Center for Tropical Agriculture DANE National Administrative Department of Statistics (Departamento Administrativo Nacional de Estadística) ECMWF European Centre for Medium-Range Weather Forecasts EIP Extrinsic incubation period ENSO El Niño–Southern Oscillation ET₀ Reference evapotranspiration FAO Food and Agriculture Organization of the United Nations FETP Field Epidemiology Training Program GAM Generalized additive model HMTP Human-to-mosquito transmission period IDP Infection-to-detection period INS Instituto Nacional de Salud LEP Liver exo-erythrocytic phase MHTP Mosquito-to-human transmission period PACF Partial autocorrelation function RDT Rapid diagnostic test SI Serial interval SIVIGILA Colombian Public Health Surveillance System (Sistema de Vigilancia en Salud Pública) SPEI Standardized Precipitation–Evapotranspiration Index WHO World Health Organization Declarations Ethical approval and consent to participate. The study was conducted in accordance with the principles of the Declaration of Helsinki. In accordance with Resolution 8430 of 1993, this research was classified as without risk. The epidemiological data is considered a secondary source of information, sourced from Sivigila (National Public Health System), where data is anonymised and published in its own repositories in accordance with policies of the Instituto Nacional de Salud de Colombia. Consent for publication: Not applicable. Availability of data and materials: The epidemiological data on malaria included in the current study are available from the Instituto Nacional de Salud through the Portal Sivigila 4.0, in the Microdatos repository: https://portalsivigila.ins.gov.co/Paginas/Buscador.aspx. Additionally, the meteorological data were obtained from the Climate Data Store of the Copernicus Climate Change Service (C3S) repository https://cds.climate.copernicus.eu/datasets. Competing interest: The authors declare that they have no competing interests. Funding: Not applicable Authors’ contribution Study conception: JH, NA, CB. Statistical methodology: JH, NA, CB. Identification and collation of data: JH, CB. Wrote the first draft of the manuscript: JH, NA. All authors critically edited the first draft. All authors read and approved the final manuscript. Acknowledgements The authors would like to express their sincere gratitude to the Infectious Diseases Data Observatory (IDDO) for its scientific and technical support in developing this study. We thank Dr Prabin Dahal, Dr Makoto Saito, Dr James Wilson and James Watson for their expert guidance on statistical modelling and their valuable feedback throughout the analysis. We also extend our deep appreciation to Dr Nancy Saravia at the Centro Internacional de Entrenamiento e Investigaciones Médicas (CIDEIM) for their critical mentorship and ongoing support in strengthening knowledge in tropical neglected diseases research. Authors’ information 1 Centro Internacional de Entrenamiento e Investigaciones Médicas (CIDEIM). Calle 18 # 122-135, Campus Universidad ICESI, Edificio O, Cali Colombia Juan Sebastian Hurtado Zapata – [email protected] & Neal Alexander – [email protected] 2 Universidad Icesi, Calle 18 # 122-135, Cali Colombia. Neal Alexander - [email protected] 3. Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT). Km 17, Cali-Palmira Highway, Cali (Palmira), Valle del Cauca, Colombia, 763537 Camilo Barrios Perez - [email protected] 4 Field Epidemiology Training Program (FETP), Instituto Nacional de Salud de Colombia. Juan Sebastian Hurtado Zapata – [email protected] References World Health Organization. World Malaria Report 2023: addressing inequity in the global malaria response [Internet]. Geneva: World Health Organization. 2023 [cited 2025 Feb 17]. Available from: https://www.who.int/publications/i/item/9789240104440 Pedraza Calderón J, Informe de Evento. 2023. Malaria [Internet]. Bogota, D.C; 2022 Jan. Available from: https://www.ins.gov.co/buscador-eventos/Informesdeevento/MALARIA%20INFORME%20DE%20EVENTO%202023.pdf Montoya-Lerma J, Solarte YA, Giraldo-Calderón GI, Quiñones ML, Ruiz-López F, Wilkerson RC, et al. Malaria vector species in Colombia-A review HHS Public Access Author manuscript. Volume 106. Mem Inst Oswaldo Cruz; 2011. Padilla-Rodríguez JC, Olivera MJ, Ahumada-Franco ML, Paredes-Medina AE. Malaria risk stratification in Colombia 2010 to 2019. PLoS ONE. 2021;16(3 March). Poveda G, Graham N, Epstein P, Rojas W, Quiñones M, Vélez ID, et al. Climate and ENSO Variability Associated with Vector-Borne Diseases in Colombia. El Nino and the Southern Oscillation. Cambridge: Cambridge University Press; 2010. pp. 183–204. Padilla-Rodríguez JC, Olivera MJ, Ahumada-Franco ML, Paredes-Medina AE. Malaria risk stratification in Colombia 2010 to 2019. PLoS ONE. 2021;16(3 March). Lozada-Martinez YA, Orozco-Chinome ID, Molina-Franky JE, Acevedo-Lopez J, Acevedo-Lopez D, Bolaño-Romero N et al. Relationship between Duffy Genotype/Phenotype and Prevalence of Plasmodium vivax Infection: A Systematic Review. Trop Med Infect Dis [Internet]. 2023 Sep 30 [cited 2025 Feb 28];8:463–75. Available from: https://doi.org/10.3390/tropicalmed8100463 Cardona-Arias JA, Higuita-Gutiérrez LF, Carmona-Fonseca J. Social determination of malaria in pregnancy in Colombia: a critical ethnographic study. Malar J. 2023;22(1). Feged-Rivadeneira A, Ángel A, González-Casabianca F, Rivera C. Malaria intensity in Colombia by regions and populations. PLoS ONE. 2018;13(9). Parham PE, Michael E. Modeling the effects of weather and climate change on malaria transmission. Environ Health Perspect. 2010;118(5):620–6. Martens WJM, Jetten TH, Rotmans J, Niessen LW. Climate change and vector-borne diseases: A global modelling perspective. Glob Environ Change. 1995;5(3):195–209. Rodríguez Vásquez C, Barrera Escobar S, Tobón-Castaño A. Low frequency of asymptomatic and submicroscopic plasmodial infections in Urabá Region in Colombia. J Trop Med. 2018;2018. Johnston GL, Smith DL, Fidock DA. Malaria’s Missing Number: Calculating the Human Component of R0 by a Within-Host Mechanistic Model of Plasmodium falciparum Infection and Transmission. PLoS Comput Biol. 2013;9(4). White NJ, Pukrittayakamee S, Hien TT, Faiz MA, Mokuolu OA, Dondorp AM. Malaria. Vol. 383, The Lancet. Elsevier B.V.; 2014. pp. 723–35. Gostic KM, McGough L, Baskerville EB, Abbott S, Joshi K, Tedijanto C et al. Practical considerations for measuring the effective reproductive number, Rt [Internet]. 2020. Available from: http://medrxiv.org/lookup/doi/ 10.1101/2020.06.18.20134858 Cori A, Ferguson NM, Fraser C, Cauchemez S. A new framework and software to estimate time-varying reproduction numbers during epidemics. Am J Epidemiol. 2013;178(9):1505–12. Nash R, Nouvellet P, Cori A. Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges. PLOS Digit Health. 2022;1:e0000052. Nash RK, Nouvellet P, Cori A. Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges. PLOS Digit Health. 2022;1(6 June). Aranda DFC. Quantification with SPEI of historical Droughts and under probable climatic change in the climatological station Zacatecas, Mexico. Tecnologia y Ciencias del Agua. 2018;9(2):210–33. Peng L, Sheffield J, Wei Z, Ek M, Wood EF. An enhanced Standardized Precipitation-Evapotranspiration Index (SPEI) drought-monitoring method integrating land surface characteristics. Earth Sys Dyn. 2024;15(5):1277–300. Chaves LF, Pascual M. Comparing models for early warning systems of neglected tropical diseases. PLoS Negl Trop Dis. 2007;1(1). Carmona-Fonseca J, Olivera MJ, Yasnot-Acosta MF. A Retrospective Review on Severe Malaria in Colombia, 2007–2020. Pathogens. 2022;11(8). Routledge I, Chevéz JER, Cucunubá ZM, Rodriguez MG, Guinovart C, Gustafson KB et al. Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting. Nat Commun. 2018;9(1). Amarilis Hurtado L, Calzada JE, Rigg CA, Castillo M, Fernando Chaves L. Climatic fluctuations and malaria transmission dynamics, prior to elimination, in Guna Yala, República de Panamá. Malar J [Internet]. 2018 [cited 2025 Mar 1];17:85. Available from: https://doi.org/10.1186/s12936-018-2235-3 Priestley CHB. On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters. Cristea NC, Kampf SK, Burges SJ. Revised Coefficients for Priestley-Taylor and Makkink-Hansen Equations for Estimating Daily Reference Evapotranspiration. J Hydrol Eng. 2013;18(10):1289–300. Suleiman AA, Hoogenboom G. Comparison of Priestley-Taylor and FAO-56 Penman-Monteith for Daily Reference Evapotranspiration Estimation in Georgia. Available from: www.Georgiaweather.net. Pereira LS. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 [Internet]. 2015. Available from: https://www.researchgate.net/publication/235704197 Haslinger K, Schöner W, Anders I. Future drought probabilities in the Greater Alpine Region based on COSMO-CLM experiments - Spatial patterns and driving forces. Meteorol Z. 2016;25(2):137–48. Stagge JH, Tallaksen LM, Xu CY. Standardized precipitation-evapotranspiration index (SPEI): Sensitivity to potential evapotranspiration model and parameters [Internet]. 2014. Available from: https://www.researchgate.net/publication/270576580 Lee S, Moriasi DN, Danandeh Mehr A, Mirchi A. Sensitivity of Standardized Precipitation and Evapotranspiration Index (SPEI) to the choice of SPEI probability distribution and evapotranspiration method. J Hydrol Reg Stud. 2024;53:101761. Bortman M. Elaboración de corredores o canales endémicos mediante planillas de cálculo. Huber JH, Johnston GL, Greenhouse B, Smith DL, Perkins TA. Quantitative, model-based estimates of variability in the generation and serial intervals of Plasmodium falciparum malaria. Malar J. 2016;15(1):1–12. St. Laurent B. Mosquito vector diversity and malaria transmission. Front Malar. 2025;3. Lerma-Montoya J, Solarte Y, Giraldo-Calderón G, Quiñones M, Ruiz-López FR, Wilkerson RC et al. Malaria vector species in Colombia - A review. Mem Inst Oswaldo Cruz [Internet]. 2011 Jul 7 [cited 2025 May 12];106(I):223–38. Available from: https://www.scielo.br/j/mioc/a/p65fN6bQLnZLdS3M9K83djt/?lang=en Piedrahita S, Correa MM. Malaria Vectors and Plasmodium Transmission in Malaria-Endemic Localities of Colombia. Trop Med Infect Dis. 2024;9(11). Caminade C, Kovats S, Rocklov J, Tompkins AM, Morse AP, Colón-González FJ et al. Impact Clim change global Malar distribution. 2014;111(9). Kibret S, Wilson GG, Ryder D, Tekie H, Petros B. Malaria impact of large dams at different eco-epidemiological settings in Ethiopia. Trop Med Health. 2017;45(1). Conde M, Pareja PX, Orjuela LI, Ahumada ML, Durán S, Jara JA et al. Larval habitat characteristics of the main malaria vectors in the most endemic regions of Colombia: Potential implications for larval control. Malar J. 2015;14(1). Sinka ME, Rubio-Palis Y, Manguin S, Patil AP, Temperley WH, Gething PW et al. The dominant Anopheles vectors of human malaria in the Americas: Occurrence data, distribution maps and bionomic précis. Parasit Vectors. 2010;3(1). Paaijmans KP, Blanford S, Bell AS, Blanford JI, Read AF, Thomas MB. Influence of climate on malaria transmission depends on daily temperature variation. Proc Natl Acad Sci U S A. 2010;107(34):15135–9. Carrasquilla MC, González R, Pineda SR, Ocampo CB, Montoya-Lerma J, Pareja-Loaiza P et al. Ecoregional and altitudinal distribution of the principal malaria vectors in Colombia. Chaves LF, Pascual M. Comparing Models for Early Warning Systems of Neglected Tropical Diseases. [cited 2025 Feb 28]; Available from: http://www.cdc Additional Declarations No competing interests reported. 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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-8643526","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581380053,"identity":"6c74ffda-241b-4d74-aa7a-67c3d18155b0","order_by":0,"name":"Juan Sebastian Hurtado Zapata","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACxgYeKM3ewHCAh0GCWC0JIPoAkVoYGGBaJBIgbIKAuf/sMenKH3ay/ZKPHx54U2ERzcDe+/gFQ40NbofNyEuTPJOQbDxzdprBwTlnJHIbeI6bWTAcS8OjhcdMsiGBOXHD7RyGw7xtQC0SaWwGjA2HcWvpPwPSUp+44eYZoJZ/xGhpyAFpOZy44QYPUEsDWAvzA7xaZuQYWzakHTee2QPyyzGJ3DaeY2wMCXj8Yth/xvBmg021bD/74ccf3tTU5faztzF/+IAnxAwb0EXYgAgURziBPDZB5g94dIyCUTAKRsHIAwDmulTcjX7BlwAAAABJRU5ErkJggg==","orcid":"","institution":"Centro Internacional de Entrenamiento e Investigaciones Medicas","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"Sebastian Hurtado","lastName":"Zapata","suffix":""},{"id":581380054,"identity":"a3412e8b-3a67-44b2-b272-6ad165be4f00","order_by":1,"name":"Neal Alexander","email":"","orcid":"","institution":"Centro Internacional de Entrenamiento e Investigaciones Medicas","correspondingAuthor":false,"prefix":"","firstName":"Neal","middleName":"","lastName":"Alexander","suffix":""},{"id":581380055,"identity":"f9e7c39d-5c34-4bba-8222-be22aa829a01","order_by":2,"name":"Camilo Barrios Pérez","email":"","orcid":"","institution":"International Center for Tropical Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Camilo","middleName":"Barrios","lastName":"Pérez","suffix":""}],"badges":[],"createdAt":"2026-01-20 00:38:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8643526/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8643526/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101397958,"identity":"fbce562c-efcf-4d02-a0ae-a5e3a7763d3a","added_by":"auto","created_at":"2026-01-29 09:38:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242021,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence of malaria in the study area, 2014-2023. The endemic channels for malaria across four departments were constructed using case data from the preceding five years to define zones. The green, orange, and red-shaded areas represent the safety, alert, and alarm zones, respectively. Observed monthly malaria cases are plotted as black dots connected by lines.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8643526/v1/f66c4bde524c977420d7a9bf.png"},{"id":101751286,"identity":"84eee4a4-bb13-4c1c-8ed0-ddf50d4ae98c","added_by":"auto","created_at":"2026-02-03 10:18:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1577089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnnually, malaria incidence rates (cases per 100,000 population) by parasite species and across four Colombian departments (2014–2023). Each panel represents a calendar year (2014-2023) and aggregates incidence data from the study period. Colours indicate the intensity of malaria incidence, with light blue representing low rates and red representing high rates, as shown in the legend. Spatial patterns highlight persistent high-incidence zones along the Pacific coast, especially in Chocó and Nariño. Data are based on confirmed malaria cases reported to SIVIGILA and department-level population estimates.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8643526/v1/a373ca7edf6f8c348cf70583.png"},{"id":101365914,"identity":"fc30ef36-78f2-4890-8c69-a59a6663e32b","added_by":"auto","created_at":"2026-01-29 00:58:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":179335,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly malaria incidence and effective reproduction number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e) over time in four departments (2014–2023). Orange lines represent monthly malaria case counts, while black points and lines indicate the estimated time-varying reproduction number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e, with shaded grey areas showing the 95% confidence intervals. The red dashed line at \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e = 1 indicate neutral value, above which sustained transmission is likely. The figure highlights temporal shifts in transmission potential, with \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e values often rising above 1 during epidemic periods and declining during periods of reduced incidence or control.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8643526/v1/ae0fc06cd3567732ca310f64.png"},{"id":101397965,"identity":"64a6b2e6-33d4-407d-bdfa-20fea302104f","added_by":"auto","created_at":"2026-01-29 09:38:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":599689,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly malaria incidence and Standardized Precipitation-Evapotranspiration Index (SPEI) in four departments (2014–2023). Displays the relationship between malaria case incidence (orange line) and climatic variation represented by the SPEI index (green line with points). The blue line represents the smoothed SPEI trend, while the red dashed line marks the neutral SPEI value (0). Positive values indicate an excess of precipitation (rainfall) over evapotranspiration, and negative values the reverse.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8643526/v1/fdb4efa9ddc13727280e2206.png"},{"id":101365918,"identity":"40dd8ab2-ed6e-4b0b-9ad9-53425c94328f","added_by":"auto","created_at":"2026-01-29 00:58:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":378098,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly malaria incidence and mean temperature trends across four departments (2014–2023). Orange lines represent the number of monthly confirmed malaria cases. Blue points and lines indicate mean temperature (°C), with the blue curve representing the smoothed trend over time. The figure illustrates the temporal relationship between temperature variability and malaria incidence, highlighting distinct transmission patterns across departments, influenced by climatic conditions.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8643526/v1/83f47caab2e42827fe72fff0.png"},{"id":101365916,"identity":"242f7abf-1da6-4a81-ba32-3404cd4d9089","added_by":"auto","created_at":"2026-01-29 00:58:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":322139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003e Effect of maximum temperature and SPEI on predicted malaria transmission \u003cem\u003e(R\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e) in the four departments, 2014–2023. These relationships were estimated using generalized additive models (GAMs) with 1–3 month lagged effects of temperature. Each plot displays a smoothed function with 95% confidence intervals (shaded area). \u003cstrong\u003eb)\u003c/strong\u003e. Present the modelled relationship between the SPEI and predicted\u003cem\u003e R\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e for the same regions.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8643526/v1/73cdf3f5ae3bfa4d9ecbb71a.png"},{"id":101880447,"identity":"0fb01430-fecf-4ea0-8406-dc7d6f4af7d0","added_by":"auto","created_at":"2026-02-04 15:02:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4387425,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8643526/v1/45f7a4b8-85b8-4910-b3b8-786955236327.pdf"},{"id":101397988,"identity":"1e242438-e503-41c8-88bd-50a6e7ee9faf","added_by":"auto","created_at":"2026-01-29 09:38:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2394824,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8643526/v1/32f901b0476708e8213d34b9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Malaria–climate interface in the Pacific and Andean regions of Colombia between 2014 and 2023","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eIn 2023, 548,000 malaria cases were recorded in the Americas region \u0026mdash; 72% due to \u003cem\u003ePlasmodium\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e.) \u003cem\u003evivax\u003c/em\u003e \u0026mdash; as were 342 deaths, with Venezuela and Brazil having the highest burden (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In 2023, Colombia had the highest number of \u003cem\u003eP\u003c/em\u003e. \u003cem\u003efalciparum cases\u003c/em\u003e, with 37,851 (35.9%) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Of these, 1,713 (1.6%) were severe malaria cases, and the departments (highest administrative divisions) with the highest incidence were Choc\u0026oacute;, Antioquia, Nari\u0026ntilde;o, C\u0026oacute;rdoba and Bol\u0026iacute;var (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApproximately 66.6% of Colombian territory is within 1,600 meters of sea level. This area encompasses around 750 municipalities (second-level administrative units) and is characterized by warm temperatures (17\u0026deg;C to 34\u0026deg;C), high relative humidity (up to 90%), and the influence of cyclical climatic phenomena such as El Ni\u0026ntilde;o and La Ni\u0026ntilde;a. These climatic and topographic conditions create a dynamic setting for malaria transmission, shaped by interacting environmental, biological, and social factors.\u003c/p\u003e \u003cp\u003eWe distinguished between two major transmission regions: the Andean region (Antioquia), characterized by temperatures slightly lower than other zones such as the Pacific and Atlantic coast(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and shorter transmission seasons, and the Pacific region (Choc\u0026oacute;, Cauca and Nari\u0026ntilde;o), known for its perennial transmission, warmer climate, and higher entomological inoculation rates (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These areas include tropical rainforests, mangroves, and lowland river systems, which provide optimal habitats for malaria vectors such as \u003cem\u003eAnopheles albimanus\u003c/em\u003e and \u003cem\u003eAnopheles nuneztovari\u003c/em\u003e. These ecological conditions support sustained malaria transmission year-round​ (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDemographic factors also have an important role in the chain of transmission. Indigenous settlements and reservations are in areas of high epidemiological risk (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). There is substantial migration, especially across the border with Venezuela and at the Darien Gap, the latter headed to Panama and onwards to North America. Departments on the Pacific coast have a large Afro-Colombian population, often with Duffy blood group, which protects against \u003cem\u003eP. vivax\u003c/em\u003e infection (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Situations of public order forced displacement, socioeconomic inequalities \u0026mdash; such as extreme poverty, lack of basic sanitation, education, work in illegal mining or crops \u0026mdash; as well as medication shortages have increased the persistence and spread of malaria (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe reproduction and survival of the \u003cem\u003eAnopheles\u003c/em\u003e vectors are strongly dependent on climatic variables such as temperature and humidity (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Temperature also affects the parasite's extrinsic cycle (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In this context, epidemiological indicators such as the time-varying effective reproductive number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e) are particularly valuable. \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e incorporates both the extrinsic incubation period (parasite development within the mosquito) and the intrinsic period (parasite development within the human host), and is closely linked to vector life conditions, reproduction capacity, and the susceptibility of the human population ((\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e estimates the average number of secondary cases generated by an infected individual during their infectious period, and how a pathogen spreads within a specific population, based on factors such as the pathogen's virulence, immunity (susceptibility), population density, and interactions among people (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This dynamic indicator provides a more accurate measure of transmission potential, enabling the identification of critical periods and the emergence of epidemic outbreaks (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Its responsiveness to temporal changes in transmission makes it a tool for monitoring and anticipating shifts in malaria dynamics under varying climatic conditions.\u003c/p\u003e \u003cp\u003eVector-borne diseases such as malaria are highly sensitive to environmental fluctuations, including changes in temperature, rainfall and moisture. While temperature is known to accelerate parasite development and influence vector survival (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), the effects of water balance are not fully captured by precipitation alone (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The Standardized Precipitation-Evapotranspiration Index (SPEI) integrates both precipitation and atmospheric evaporative demand and serves as a more comprehensive proxy for drought intensity and moisture availability \u0026mdash; critical factors in the formation and persistence of mosquito breeding habitats (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Drought conditions, as measured by SPEI, have been associated with elevated risks of vector-borne diseases in previous studies, particularly through mechanisms such as the concentration of breeding sites, shifts in vector density, and increased human\u0026ndash;vector contact around scarce water sources (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Although SPEI remains underutilized in malaria research, it has shown value in agricultural and hydrological forecasting, offering insights into climatic conditions that may influence malaria transmission dynamics in endemic regions(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eClimatic variability can generate periods of high transmission, followed by abrupt drops in incidence, posing challenges for predicting and managing the disease ((\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The use of joint epidemiological and climatic models (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) has the potential to facilitate not only the retrospective analysis of malaria trajectories, but also early warning measures based on indicators such as \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e, temperature, and less well-known indices such as SPEI (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor high-incidence regions or endemic areas of Colombia, the present study analyzes the association between malaria \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e and climatic variation in terms of SPEI and temperature. The integration of climatic and epidemiological data facilitates the identification of risk patterns or seasonal trends, potentially guiding local control strategies.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis study employed an ecological (i.e., area-level) time-series design to assess the association between climatic variability and malaria transmission dynamics across parasite species in Colombia between 2014 and 2023. Specifically, we examined the relationship between the time-varying effective reproduction number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e) for malaria (\u003cem\u003eP. vivax and P. falciparum)\u003c/em\u003e and climatic indicators: in particular, the Standardized Precipitation-Evapotranspiration Index (SPEI) and temperature. The analysis focused on four administrative units: the departments of Antioquia, Choc\u0026oacute;, Cauca and Nari\u0026ntilde;o. These locations were selected for their status as endemic regions or for observed shifts in malaria incidence over the study period. In Cauca and Nari\u0026ntilde;o, only \u003cem\u003eP. falciparum\u003c/em\u003e was analyzed, because this species accounted for 90% to 96% of the total number of cases over the previous ten years. The design allowed for temporal and spatial comparisons to identify climate-driven transmission patterns in both Pacific and Andean ecological contexts.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eData were obtained from the national Public Health Surveillance System (SIVIGILA), the AgERA5 dataset (Copernicus Climate Change Service), and population estimates from the National Administrative Department of Statistics (DANE). From SIVIGILA, we obtained individual-level data on malaria cases reported via routine surveillance, confirmed by Rapid Diagnostic Test (RDT) or thick blood smear in accordance with the case definition of the Colombian INS (National Institute of Health).\u003c/p\u003e \u003cp\u003eSPEI is the \u003cem\u003eZ\u003c/em\u003e score of the difference between precipitation and reference evapotranspiration (\u003cem\u003eET\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e). \u003cem\u003eET\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (in mm per day) was estimated using the Priestley-Taylor Eq.\u0026nbsp;(25) due to its simplicity(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) its acceptable performance in low latitudes (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), and its minimum input requirements (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{ET}_{0}=a\\frac{\\varDelta\\:}{\\varDelta\\:+\\gamma\\:}\\frac{{R}_{n}}{\\lambda\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.26 is the dimensionless Priestley-Taylor evaporative coefficient, and \u003cem\u003eλ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.45 MJ/kg is the latent heat of vaporization at 20\u0026deg;C. Values of ∆ (slope of the vapor pressure-temperature curve), \u003cem\u003eγ\u003c/em\u003e (psychrometric constant for different altitudes), and \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e (net radiation, as opposed to the \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e reproduction number) were calculated following the FAO-56 procedure (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). To obtain the SPEI, a logistic curve is fitted to observed differences between precipitation and \u003cem\u003eET\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and the resulting cumulative distribution function (CDF) is converted to a \u003cem\u003eZ\u003c/em\u003e score via the CDF of a standard normal (Gaussian) distribution (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrecipitation, radiation, and altitude data for calculating the SPEI were obtained from satellite-based raster images in the AgERA5 dataset. This integrates daily meteorological station data to support agronomic applications and agricultural models. The information is provided in grid cells at a spatial resolution of 0.05\u0026deg; (approximately 10 km), corresponding to hourly ERA5 data\u0026mdash;the fifth-generation atmospheric reanalysis produced by the ECMWF (European Centre for Medium-Range Weather Forecasts)(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDescriptive analysis and spatial visualization\u003c/h3\u003e\n\u003cp\u003eA descriptive analysis characterised malaria transmission patterns across departments, parasite species and time. Monthly and annual malaria case counts were summarised by department and Plasmodium species, and incidence rates were calculated using department-specific population estimates. Sociodemographic details of reported cases, including age, sex, area of residence, ethnicity, insurance status, pregnancy status and clinical outcomes, were evaluated using medians and interquartile ranges for continuous variables and frequencies and percentages for categorical data. Differences among departments were assessed using the Kruskal\u0026ndash;Wallis test for continuous variables and Pearson\u0026rsquo;s chi-squared test for categorical variables.\u003c/p\u003e \u003cp\u003eTo examine temporal and spatial variations in malaria transmission, time-series plots of malaria incidence and the effective reproduction number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e) were created. Additionally, spatial heat maps of annual malaria incidence rates were produced to display geographic patterns and the persistence of high-transmission areas over the study period.\u003c/p\u003e\n\u003ch3\u003eEndemic channel\u003c/h3\u003e\n\u003cp\u003eTo identify epidemic episodes, contextualize malaria incidence, including fluctuations, and find unusual transmission levels, endemic channels (epidemic thresholds) were constructed for each department. These channels enable the classification of observed malaria case counts into ranges representing expected and excessive transmission, relative to historical data trends. Outbreak or epidemic status is defined by the difference between the observed and expected numbers of cases over a specific period, area (department), and population. More specifically, the methodology of Bortman (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) was used to define zones of safety, alert, alarm, and epidemic based on measures of central tendency and dispersion.\u003c/p\u003e \u003cp\u003eThe channels were calculated using the epiCo R package (version 1.0.1) from the epiverse-trace project, to derive the endemic threshold from monthly malaria case data aggregated by department. Specifically, we applied a 5-year baseline window, excluding the epidemic year under evaluation. Observed monthly case counts were overlaid and assessed as being in the safety zone (below 75th percentile), alert zone (75th\u0026ndash;90th percentile), or alarm zone (above 90th percentile).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSerial interval and time-varying effective reproduction number\u003c/b\u003e \u003cb\u003eR\u003c/b\u003e\u003csub\u003e\u003cb\u003et\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eThe serial interval (SI) is defined as the time between the symptom onset in a primary malaria case and the symptom onset in a secondary case directly infected by the former. For a pair of individuals, \u003cem\u003ej\u003c/em\u003e infected earlier and \u003cem\u003ei\u003c/em\u003e later, with symptom onset times \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, the SI is \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e - \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e. The probability distribution of the serial interval links symptom timing and transmission likelihood (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In vector-borne infections such as malaria, the SI does not arise from a single biological process but instead is the sum of different times, including (a) the time between symptom onset and the period of infectiousness to mosquitoes (gametocyte emergence), the liver exo-erythrocytic phase LEP, (b) the time required for mosquitoes to become infectious after acquiring parasites, that is considered as human to mosquito transmission period, HMTP, (c) the period after to susceptible mosquito acquire the parasite from the human and duration of mosquito infectivity (constrained by lifespan) EIP, (d) the incubation period in the newly infected human host prior to symptom onset, MHTP and finally (e) the interval between infection to detection by either way such as clinic assessment or diagnosis test, IDP (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe model parameters were chosen to reflect the regional dynamics of malaria transmission in Colombia, by department and parasite species, using a simulation-based mechanistic framework. This model incorporated ecological assumptions regarding the dominant vector species in each department, which influence key transmission parameters such as mosquito daily survival, EIP, and biting rate.\u003c/p\u003e \u003cp\u003eSpecifically, \u003cem\u003eAnopheles albimanus\u003c/em\u003e was assumed to be the predominant vector in Choc\u0026oacute; and Nari\u0026ntilde;o, in Cauca, \u003cem\u003eAn. nuneztovari\u003c/em\u003e, it was considered based on vector distribution information in Colombia. For Antioquia, particularly in its Urab\u0026aacute; region, a model was developed. \u003cem\u003edarlingi\u003c/em\u003e, given their documented presence in this region(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mosquito survival probabilities, biting rates, and EIP durations were chosen accordingly in the simulation model for each department, resulting in differentiated serial interval distributions that reflect local eco-epidemiological conditions.\u003c/p\u003e \u003cp\u003eEach component of the SI was parameterized based on mechanistic evidence. The liver exo-erythrocytic phase (LEP) for \u003cem\u003eP. vivax\u003c/em\u003e was assumed to be ~\u0026thinsp;8.5 days at 26\u0026deg;C (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The human infectious period (HMTP) was modelled using Gaussian infectiousness curves, reflecting earlier and longer gametocyte carriage in \u003cem\u003eP. vivax\u003c/em\u003e than in \u003cem\u003eP. falciparum\u003c/em\u003e (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). For EIP, a normal distribution was used, with region-specific means and standard deviations, drawing on entomological studies indicating temperature-dependent development rates. For instance, \u003cem\u003eP. vivax\u003c/em\u003e EIP was modelled with mean durations of 12 days (Pacific) and 20 days (Andean), aligned with published thermal performance models (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and the mosquito lifespan was estimated using a geometric distribution based on daily survival probabilities specific to vector species and climate. Finally, treatment delays were simulated using Poisson distributions, assuming shorter delays in the IPD in Antioquia (2.5 days compared with 3.5 days in other departments) due to greater health service accessibility (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis framework was used to simulate 10,000 full transmission stages and cycles for each combination of region and parasite, representing a sequence through infection and symptom development (Additional file Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe estimated SI distributions for each parasite species and region were incorporated into the estimation of the time-varying effective reproduction number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e) using the EpiEstim R package (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Values above 1 indicate increasing transmission, while values below 1 indicate transmission decline (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eTemperature\u003c/h3\u003e\n\u003cp\u003eDaily maximum temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e) values from meteorological and satellite stations were obtained from the AgERA5 dataset across the full extent of each department. The data are available in ECMWF format for SPEI. For SPEI, the rasters were clipped to each department's vector layer to extract the average \u003cem\u003eT\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e. As for other variables, the temperature data of the pixels in each grid were weighted according to the proportion of the area that is within the selected. Monthly averages were also calculated.\u003c/p\u003e\n\u003ch3\u003eEffect of climate (SPEI and temperature) on transmission (Rt)\u003c/h3\u003e\n\u003cp\u003eFor the malaria case series, the exploratory analysis excluded incomplete or inconsistent records, and the data were evaluated to ensure temporal continuity of the monthly and annual series. Then, descriptive analysis of the sociodemographic characteristics of malaria cases was used to characterize the epidemiology of the disease.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e, SPEI, and temperature, the exploratory analysis included identifying seasonality and calculating partial autocorrelation (PACF) and autocorrelation (ACF). Lags for the three variables at 1, 2, and 3 months were selected as candidate variables for evaluating the effects of climate on \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eGeneralized additive models (GAMs) (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) were used to assess the association between climate variables and malaria transmission in each department. This model incorporates smoothing terms, facilitating the modelling of complex patterns in epidemiological data on incidence, transmission, and climate (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), and handling lagged effects. Additionally, multiple GAM specifications were assessed by varying the smoothing function types and the lag terms (1-, 2-, and 3-month). For each GAM specification, the deviance explained was calculated to determine whether the patterns observed in the fitted smooths could arise by chance. In addition, the stability of the estimated smooth terms (i.e., temperature and SPEI) and coefficients (i.e., consistency of effect sizes across model specifications)\u003c/p\u003e \u003cp\u003eModel performance was assessed using the percentage of deviance explained. The final model specification incorporated tensor product smooths (\u003cem\u003ete\u003c/em\u003e) to flexibly model the joint effects of climatic variables and their lags (e.g., SPEI and temperature). Within the tensor product, smooth, cubic splines were used. A separate model was used for each combination of \u003cem\u003ePlasmodium\u003c/em\u003e species and department. This approach was chosen over univariate smooths due to its ability to handle predictors on different scales and its suitability for modelling interactions. Additionally, to adjust for the observed autocorrelation in malaria transmission, the reproductive number at a one-month lag was included as a parametric term.\u003c/p\u003e \u003cp\u003eFinally, models were compared using AIC for each department and parasite species evaluated, selecting the best model based on the highest deviance explained and biological plausibility (consistent with mosquito and parasite life cycles).\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using R (version 4.3.2).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eCharacteristics of malaria cases\u003c/h2\u003e\n\u003cp\u003eBetween the start of 2014 and the end of 2023, 476,810 cases were reported to SIVIGILA from the study area (four departments). Of these, 301,427 (63.2%) were due to \u003cem\u003eP\u003c/em\u003e. \u003cem\u003efalciparum\u003c/em\u003e and 175,383 (36.8%) to \u003cem\u003eP\u003c/em\u003e. \u003cem\u003evivax\u003c/em\u003e. Just over half (53.5%) of the cases were from the department of Choc\u0026oacute;, and almost another quarter (23.6%) were from Nari\u0026ntilde;o. The overall annual incidence rate was 5,398 per 100,000 inhabitants in the whole study area. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e maps the incidence for each year.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCases of malaria by department and \u003cem\u003ePlasmodium\u003c/em\u003e species, 2014\u0026ndash;2023.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDepartment\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. vivax\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\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\"\u003e\n\u003cp\u003eAntioquia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e62,582 (79.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16,413 (20.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78,995\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCauca\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e552 (2.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22,821 (97.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23,373\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChoc\u0026oacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100,764 (39.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e154,118 (60.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e254,882\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNari\u0026ntilde;o\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11,485 (9.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e108,075 (90.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e119,560\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e175,383 (36.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e301,427 (63.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e476,810\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAmong the four departments, Choc\u0026oacute; experienced major outbreaks by \u003cem\u003eP. falciparum\u003c/em\u003e in 2015 and early 2016, with monthly cases surpassing the alarm threshold of 1,200 cases (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003eP. vivax\u003c/em\u003e showed similar patterns but with lower incidence. From 2019 to 2021, transmission in Choc\u0026oacute; was relatively stable, before a sharp post-pandemic increase in 2022 and a more pronounced outbreak in late 2023, during which the incidence of \u003cem\u003eP. falciparum\u003c/em\u003e and \u003cem\u003eP. vivax\u003c/em\u003e became comparable. In Nari\u0026ntilde;o, incidence steadily increased from 2015, rising from 110 to over 250 monthly cases between 2018 and 2020, with recurrent short- to medium-duration outbreaks. Case counts decreased significantly in 2021\u0026ndash;2022 (returning to the safety zone) but rebounded into the alert zone in 2023.\u003c/p\u003e\n\u003cp\u003eCauca exhibited lower overall malaria transmission but showed a consistent pattern of moderate \u003cem\u003eP. falciparum\u003c/em\u003e outbreaks during 2015\u0026ndash;2016, followed by a sharp, isolated peak in mid-2019. Afterwards, incidence remained below endemic thresholds until a resurgence in late 2023. In Antioquia, transmission remained largely within endemic levels for both species, although both occasionally exceeded the alert level.\u003c/p\u003e\n\u003cp\u003eAcross departments, the median age ranged from 19 to 26 years (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Most cases were male (60%), of African descent (51%), and rural (76%). A total of 146 deaths were reported over the 10 years (case fatality of less than 0.1%), with 96 (65.7%) of those deaths in Choc\u0026oacute;.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSociodemographic characteristic malaria cases in 2014\u0026ndash;2023\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eANTIOQUIA\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;78,995\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCAUCA\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;23,373\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCHOCO\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;254,882\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNARI\u0026Ntilde;O\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;119,560\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOverall\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;476,810\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\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\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\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\"\u003e\n\u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\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\"\u003e\n\u003cp\u003eF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29,840 (38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10,159 (43%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113,607 (45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50,167 (42%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e203,773 (43%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49,155 (62%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13,214 (57%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141,275 (55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69,393 (58%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e273,037 (57%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\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\"\u003e\n\u003cp\u003eRural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72,588 (92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18,576 (79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163,868 (64%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90,723 (76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e345,755 (73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrbana\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,407 (8.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,797 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91,014 (36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28,837 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131,055 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\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\"\u003e\n\u003cp\u003eAfro-descendant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,134 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21,727 (93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e155,004 (61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108,053 (90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e292,918 (61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndigenous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13,217 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e551 (2.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81,711 (32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,696 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98,175 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMixed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57,413 (73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,055 (4.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17,434 (6.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,597 (7.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84,499 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRaizel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e226 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e400 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRom / Gypsy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e507 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e818 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInsurance type\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\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\"\u003e\n\u003cp\u003eContributive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,820 (7.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,449 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14,702 (5.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,082 (5.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30,053 (6.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eException\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,617 (2.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e408 (1.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,753 (1.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,881 (2.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9,659 (2.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot insured\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12,275 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,494 (15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34,570 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17,859 (15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68,198 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpecial\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e384 (0.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,937 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,152 (1.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,539 (0.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubsidize\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58,899 (75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16,956 (73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198,920 (78%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90,586 (76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e365,361 (77%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePregnant\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e618 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144 (0.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,446 (0.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,224 (1.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,432 (0.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\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\"\u003e\n\u003cp\u003e\u003cstrong\u003ePlasmodium\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003evivax\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62,582 (79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e552 (2.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100,764 (40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11,485 (9.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e175,383 (37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003efalciparum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16,413 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22,821 (98%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e154,118 (60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108,075 (90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e301,427 (63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFinal condition\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78,979 (100%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23,369 (100%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e254,815 (100%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e119,548 (100%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e476,711 (100%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMedian (Q1, Q3); n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003csup\u003e2\u003c/sup\u003eKruskal-Wallis rank sum test; Pearson's Chi-squared test\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTime-varying effective reproduction number (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/sub\u003e\u003c/em\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Choc\u0026oacute;, particularly for \u003cem\u003eP. falciparum\u003c/em\u003e, the number of cases spiked sharply during the major outbreak of early 2016, with \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e exceeding 1.5 (95% CI: 1.45\u0026ndash;1.65). From late 2022 through 2023, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e showed a renewed upward trend. \u003cem\u003eP. vivax\u003c/em\u003e in Choc\u0026oacute; displayed short-lived peaks generally between 1.5 and 2.0 (95% CI: 1.61\u0026ndash;2.08). A notable increase was recorded in 2019, when transmission of both parasite species rose substantially.\u003c/p\u003e\n\u003cp\u003eIn Nari\u0026ntilde;o, \u003cem\u003eP. falciparum\u003c/em\u003e transmission showed multiple intermittent increases, most prominently between 2018 and 2020, with peaks approaching \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e = 1.97 (95% CI: 1.78\u0026ndash;2.17). These increases typically preceded corresponding increases in reported cases by 1 to 3 months. Cauca experienced the most pronounced rise in \u003cem\u003eP. falciparum R\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e in 2015, reaching approximately 3.5 (95% CI: 2.60\u0026ndash;4.50). However, case numbers during this period remained relatively low and stable. In 2020, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e again rose above 2.5 (95% CI: 2.04\u0026ndash;3.07), coinciding with the largest recorded case peak.\u003c/p\u003e\n\u003cp\u003eIn Antioquia, \u003cem\u003eP. falciparum\u003c/em\u003e exhibited marked fluctuations in transmission intensity throughout the evaluation period, with the highest \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e recorded in May 2020 at 2.21 (95% CI: 1.74\u0026ndash;2.78). Although \u003cem\u003eP. falciparum\u003c/em\u003e case numbers were substantially lower than those of \u003cem\u003eP. vivax\u003c/em\u003e, the latter species maintained values close to 1.0 for most of the period, except in May 2020, when \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e rose to 1.96 (95% CI: 1.70\u0026ndash;2.30).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePatterns of SPEI and temperature\u003c/h3\u003e\n\u003cp\u003eSPEI exhibited seasonal fluctuations across departments (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), while malaria incidence fluctuated in parallel but with lags in some departments. In Choc\u0026oacute;, \u003cem\u003eP. falciparum\u003c/em\u003e incidence peaked in 2016, 2018, and late 2022\u0026ndash;2023, and tended to occur during or shortly after phases of moderately negative SPEI, i.e., drier\u0026ndash;than\u0026ndash;normal conditions. For \u003cem\u003eP. vivax\u003c/em\u003e in Choc\u0026oacute;, peaks in incidence were smaller in magnitude but still clustered around SPEI downturns, particularly during 2019\u0026ndash;2020.\u003c/p\u003e\n\u003cp\u003eIn Antioquia, \u003cem\u003eP. falciparum\u003c/em\u003e transmission displayed multiple moderate peaks that often followed short-term SPEI declines, whereas \u003cem\u003eP. vivax\u003c/em\u003e exhibited more pronounced peaks in 2018 and 2023, each preceded by a gradual shift to negative values, indicating a potential climate signal with a lagged effect of several months.\u003c/p\u003e\n\u003cp\u003eCauca presented less consistent relationships. The sharpest \u003cem\u003eP. falciparum\u003c/em\u003e increase in 2020 coincided with a phase of mildly lower SPEI, but other peaks occurred under neutral SPEI, suggesting that local non-climatic factors played a stronger role here. In Nari\u0026ntilde;o, \u003cem\u003eP. falciparum\u003c/em\u003e transmission between 2018 and 2020 rose during a sustained period of negative SPEI, while the subsequent decline in cases (2021\u0026ndash;2023) coincided with a return to near-neutral or positive SPEI conditions.\u003c/p\u003e\n\u003cp\u003eThese results indicate that malaria peaks tended to be more likely during or following periods of negative SPEI, particularly in Choc\u0026oacute; and Antioquia, and often with a lead time of one to three months between climate signal onset and epidemiological response. However, the strength and consistency of this relationship varied by department.\u003c/p\u003e\n\u003cp\u003eThe relationship between temperature and malaria incidence (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) showed notable heterogeneity among departments. In Choc\u0026oacute;, both P. \u003cem\u003efalciparum\u003c/em\u003e and \u003cem\u003eP. vivax\u003c/em\u003e displayed major incidence peaks in early 2016, late 2019\u0026ndash;2020, and 2023. These peaks occurred during periods of relatively high temperatures (25\u0026ndash;27\u0026deg;C). By Antioquia, temperatures remained comparatively stable (21\u0026ndash;23\u0026deg;C) throughout the study period, yet moderate \u003cem\u003eP. vivax\u003c/em\u003e peaks in 2018 and 2023, and \u003cem\u003eP. falciparum\u003c/em\u003e increases in 2020, coincided with the warmer end of this range, suggesting that small seasonal variations can be sufficient to influence the dynamics of the cases.\u003c/p\u003e\n\u003cp\u003eFor Cauca, \u003cem\u003eP. falciparum\u003c/em\u003e incidence peaks\u0026mdash;particularly in 2020\u0026mdash; were not preceded by marked temperature anomalies, suggesting that climatic variables played a limited role in driving transmission during that period. A similar pattern was observed in Nari\u0026ntilde;o, where P. falciparum transmission between 2015 and 2019 coincided with moderately warmer years (22\u0026ndash;23\u0026deg;C), but the notable decline in cases from 2020 onward occurred despite persistently similar temperature conditions, pointing to non-climatic drivers as the dominant influence during the later period.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that, while temperature appears to play a consistent and amplifying role in Choc\u0026oacute; and, to a lesser degree, in Antioquia and Nari\u0026ntilde;o, its association with malaria transmission is less evident in Cauca. The influence of temperature also appears to be modulated by local ecological and environmental contexts, suggesting that its effect on malaria risk may not be uniform across regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluating associations using a generalized additive model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneralized additive models (GAMs) with lagged climate predictors revealed heterogeneous associations between climatic variables and malaria transmission across departments and \u003cem\u003ePlasmodium\u003c/em\u003e species. The best-fit models incorporated 2-month lags for temperature and SPEI in Choc\u0026oacute;, Antioquia, and Nari\u0026ntilde;o, and 3-month lags in Cauca, along with a 1-month lag for \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e to account for short-term persistence in transmission\u003c/p\u003e\n\u003cp\u003eIn Choc\u0026oacute;, temperature showed a strong and statistically significant relationship with \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e for both P. falciparum (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and P. vivax (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining 45.6% of the deviance. Si Effect plots indicated a non-linear response, with lower R\u003csub\u003et\u003c/sub\u003e values around 24.5\u0026deg;C and increases at both lower (\u0026lt;\u0026thinsp;24\u0026deg;C) and higher (\u0026gt;\u0026thinsp;26\u0026deg;C) temperatures. SPEI, lagged by two months, was not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) but showed a slight negative trend for P. vivax under moister conditions.\u003c/p\u003e\n\u003cp\u003eIn Antioquia, SPEI significantly influenced R\u003csub\u003et\u003c/sub\u003e for both P. falciparum (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and P. vivax (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.44, p\u0026thinsp;=\u0026thinsp;0.002), although the explained variance was lower (\u003cem\u003eR\u003c/em\u003e\u0026sup2; = 0.362, deviance\u0026thinsp;=\u0026thinsp;49.6%). Temperature effects were significant for P. falciparum (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.97, p\u0026thinsp;=\u0026thinsp;0.008) but not for P. vivax (p\u0026thinsp;=\u0026thinsp;0.475). The temperature and R\u003csub\u003et\u003c/sub\u003e curve for P. falciparum showed a sharp decline above ~\u0026thinsp;21.5\u0026deg;C followed by a plateau.\u003c/p\u003e\n\u003cp\u003eIn Cauca, SPEI was not significantly associated with Rt (p\u0026thinsp;=\u0026thinsp;0.189), but temperature had a strong effect on P. falciparum Rt (F\u0026thinsp;=\u0026thinsp;3.34, p\u0026thinsp;=\u0026thinsp;0.004; R\u0026sup2; = 0.385, deviance\u0026thinsp;=\u0026thinsp;50.8%). The temperature response curve showed increased transmission above 22.8\u0026deg;C, peaking near 23\u0026deg;C. The longer, 3-month lag suggests that, in this cooler setting, it can influence vector and parasite dynamics.\u003c/p\u003e\n\u003cp\u003eIn Nari\u0026ntilde;o, SPEI did not significantly affect \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.122), whereas temperature was strongly and positively associated with \u003cem\u003eP. falciparum\u003c/em\u003e, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; R\u0026sup2; = 0.371, deviance\u0026thinsp;=\u0026thinsp;45.9%). The temperature effect plot showed a steady increase in \u003cem\u003eRt\u003c/em\u003e with increasing mean temperature, peaking at ~\u0026thinsp;22.8\u0026deg;C. The 2-month lag here reflects a similar climate\u0026ndash;transmission delay to that in Choc\u0026oacute; and Antioquia, despite Nari\u0026ntilde;o\u0026rsquo;s greater geographic and climatic heterogeneity.\u003c/p\u003e\n\u003cp\u003eFinally, the temperature emerged as the dominant climatic driver of \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e variability in most departments, particularly for \u003cem\u003eP. falciparum\u003c/em\u003e. SPEI effects were more variable, with stronger signals in Antioquia and subtle trends in Choc\u0026oacute;. Warming or lower values in the SPEI (lesser moisture) precede increases in \u003cem\u003eRt\u003c/em\u003e by approximately two to three months, providing a potential early-warning window before case surges occur.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePerformance of GAMs for Climate Effects on Malaria R\u003csub\u003et\u003c/sub\u003e by department\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDepartment\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParasite\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePercent deviance explained\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eChoc\u0026oacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPEI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP.\u0026nbsp;falciparum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.763\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.381\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e45.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPEI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. vivax\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.303\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTemperature\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e3.282\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTemperature\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. vivax\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e3.271\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eAntioquia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPEI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e4.028\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.362\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e49.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPEI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. vivax\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.443\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTemperature\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.972\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.008*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTemperature\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. vivax\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.837\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.475\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCauca\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPEI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.189\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.385\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50.8%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTemperature\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e3.343\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.004*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNari\u0026ntilde;o\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPEI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.977\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e45.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTemperature\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e3.603\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ePerformance metrics of three generalized additive model (GAM) specifications applied separately for each department. For each model, the table provides the adjusted \u003cem\u003eR\u003c/em\u003e\u0026sup2;, the percentage deviance explained, and the p-value for the smooth terms relative to the null model. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The temperature presented in the table corresponds to the maximum temperature for each entity\u003c/p\u003e\n\u003ch3\u003e\u0026nbsp;\u003c/h3\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study underscores the significant role of climate variability, especially temperature fluctuations and moisture availability conditions measured by the Standardized Precipitation and Evapotranspiration Index (SPEI), with malaria transmission dynamics, as reflected in the effective reproduction number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e). These findings align with previous global and regional studies, reinforcing the link between climate variability and vector-borne disease dynamics (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eIn addition, our findings also show that SPEI can be relevant in certain regions for explaining malaria transmission. (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e) Unlike other climatic measures related to rainfall or drought, SPEI integrates both precipitation and potential evapotranspiration, thus capturing net water availability in the environment. This is important because mosquito larval development depends not only on rainfall inputs but also on the persistence of standing water. Positive SPEI values (moisture-average conditions) can sustain breeding habitats for longer periods, enhancing vector survival and reproduction, while negative SPEI values (drier conditions) may accelerate habitat drying and reduce larval survival. However, in some cases, moderate negative anomalies can also concentrate breeding in more permanent water bodies, potentially increasing vector\u0026ndash;host contact rates (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eLow SPEI values\u0026mdash;indicating drier conditions\u0026mdash;were associated with higher \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e in some settings, particularly Choc\u0026oacute;(\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e). Similar dynamics have been reported in sub-Saharan Africa (\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e), where drought conditions increased malaria transmission risk, and large water reservoirs created localised, stable breeding habitats in otherwise drier landscapes, leading to increased vector densities and transmission risk in nearby communities.\u003c/p\u003e\n\u003cp\u003eIn \u003cem\u003eAnopheles albimanus\u003c/em\u003e, the primary malaria vector in Choc\u0026oacute;, larvae have been documented in a wide variety of permanent and human-made water bodies, including fishponds, lagoons, ditches, excavation sites, and puddles, even during dry spells and when water is marginal in quality(\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e). This ecological tolerance allows vector populations to persist in fewer but stable aquatic habitats\u0026mdash;such as shaded excavations or domestic wash sites\u0026mdash;when rainfall is scarce. In low-SPEI scenarios, when temporary breeding locations disappear, these remaining bodies of stagnant water may act as concentrated \u0026lsquo;hotspots\u0026rsquo; for larval development, maintaining transmission potential in otherwise challenging conditions (\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTemperature is a key driver of malaria transmission, affecting both vector populations and parasite development. Warmer temperatures accelerate the extrinsic incubation of \u003cem\u003ePlasmodium\u003c/em\u003e parasites, reducing the time required for mosquitoes to become infectious (\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e). However, the relationship between temperature and malaria transmission is not linear, and our study found important regional differences.\u003c/p\u003e\n\u003cp\u003eAcross all departments, temperature consistently predicted malaria transmission, with warmer conditions generally associated with higher \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e values. In Choc\u0026oacute;, for both \u003cem\u003eP. falciparum\u003c/em\u003e and \u003cem\u003eP. vivax\u003c/em\u003e, we observed a strong positive relationship between temperature and \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e,\u003c/sub\u003e suggesting that higher temperatures likely intensified transmission. This finding is consistent with previous studies indicating that the biology of \u003cem\u003eAn. albimanus\u003c/em\u003e, which thrives in sunlit, stagnant water bodies and is highly efficient in coastal floodplain environments, and \u003cem\u003eAn. darlingi\u003c/em\u003e, which is also present and well-adapted to rainforest ecosystems. Optimal transmission conditions for these species have been observed at temperatures between 20\u0026ndash;30\u0026deg;C, particularly around 26\u0026deg;C(\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e). In Antioquia, a similarly strong temperature\u0026ndash;\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e association was observed, where \u003cem\u003eAn. nuneztovari\u003c/em\u003e and \u003cem\u003eAn. darlingi\u003c/em\u003e are the primary vectors. These species prefer humid forest and foothill habitats, with more stable microclimates in the 22\u0026ndash;29\u0026deg;C range and may be more sensitive to temperature fluctuations due to their breeding and resting behaviour (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e). These findings suggest that vector-specific ecological preferences modulate the strength and shape of the temperature\u0026ndash;transmission relationship, an aspect that can merits further investigation in Colombia\u0026rsquo;s diverse eco-epidemiological settings (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAn important finding of our study was that \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e often peaked between one to three months before an increase in reported malaria cases; these findings align with previous studies showing that climatic factors have effects on malaria(\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e). From a biological perspective, the lag intervals correspond to the expected delays between climate anomalies and their downstream impact on malaria transmission, considering the extrinsic incubation period (EIP) of the parasite in the mosquito (typically 10\u0026ndash;20 days), mosquito population response time to environmental changes (1\u0026ndash;3 weeks), and human intrinsic incubation period (10\u0026ndash;15 days), leading to an anticipated lag of 1 to 3 months between climate changes and observable shifts in transmission (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eHowever, regional variability in model performance emphasises the need for local calibration and suggests that non-climatic drivers, such as vector control, land use, or socio-demographic shifts, may modulate climate impacts in certain settings.\u003c/p\u003e\n\u003cp\u003eSeveral limitations of our study should be acknowledged. The use of surveillance data from SIVIGILA introduces potential biases. Underreporting is a common challenge in malaria surveillance, and reporting completeness may vary by region and altitude. If malaria cases are more frequently reported in cooler, high-altitude areas, this could bias estimates of the relationship between temperature and malaria. Also, the entire department was used to extract temperature data, but malaria is not necessarily endemic throughout the department. Additionally, non-climatic factors, such as land-use changes, migration, and healthcare access, likely modulate malaria transmission and should be incorporated into future models. Likewise, finer-scale vector data are needed to improve the predictive accuracy of climate-based models. Lastly, further studies should explore potential threshold effects, identifying specific temperature and SPEI cutoffs that significantly alter malaria risk.\u003c/p\u003e\n"},{"header":"CONCLUSION","content":"\u003cp\u003eOur findings highlight the role of climate variability, particularly temperature fluctuations and the SPEI balance, in shaping malaria transmission dynamics in Colombia. Temperature was consistently associated with increased transmission potential, while lower moisture conditions, as reflected by lower SPEI values, were also linked to higher \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e. These patterns suggest that climatic factors can intensify malaria risk by both enhancing vector survival and altering human\u0026ndash;mosquito contact. The study underscores the importance of incorporating climate indicators into malaria surveillance systems to support early warning and targeted control strategies.\u003c/p\u003e\n\u003cp\u003eA major implication of our study is the potential use of \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e as an early warning indicator for changes in malaria transmission. Although we observed that \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e often increased prior to rises in reported cases, further research is needed to formally assess lead times and predictive performance. Our findings suggest that combining \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e with climate indicators such as temperature and SPEI may offer valuable insights into transmission dynamics, but further evaluation is required before these tools can be integrated into operational surveillance or early warning systems.\u003c/p\u003e\n\u003cp\u003eDespite these insights, malaria transmission remains a multifactorial process influenced by non-climatic variables such as land use, human migration, and healthcare accessibility. Future research should incorporate these factors to refine predictive models. As climate change continues to alter malaria risk patterns, proactive, data-driven interventions will be essential to sustain progress toward control and malaria elimination in Colombia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAutocorrelation function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCumulative distribution function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIDEIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentro Internacional de Entrenamiento e Investigaciones M\u0026eacute;dicas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Center for Tropical Agriculture\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDANE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Administrative Department of Statistics (Departamento Administrativo Nacional de Estad\u0026iacute;stica)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECMWF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Centre for Medium-Range Weather Forecasts\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtrinsic incubation period\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eENSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEl Ni\u0026ntilde;o\u0026ndash;Southern Oscillation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eET₀\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReference evapotranspiration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFAO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFood and Agriculture Organization of the United Nations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFETP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eField Epidemiology Training Program\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized additive model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHMTP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman-to-mosquito transmission period\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInfection-to-detection period\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstituto Nacional de Salud\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLEP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLiver exo-erythrocytic phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMHTP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMosquito-to-human transmission period\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePACF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePartial autocorrelation function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRDT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRapid diagnostic test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSerial interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSIVIGILA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColombian Public Health Surveillance System (Sistema de Vigilancia en Salud P\u0026uacute;blica)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPEI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandardized Precipitation\u0026ndash;Evapotranspiration Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki. In accordance with Resolution 8430 of 1993, this research was classified as without risk. The epidemiological data is considered a secondary source of information, sourced from Sivigila (National Public Health System), where data is anonymised and published in its own repositories in accordance with policies of the Instituto Nacional de Salud de Colombia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe epidemiological data on malaria included in the current study are available from the Instituto Nacional de Salud through the Portal Sivigila 4.0, in the Microdatos repository:\u0026nbsp;https://portalsivigila.ins.gov.co/Paginas/Buscador.aspx. Additionally, the meteorological data were obtained from the Climate Data Store of the Copernicus Climate Change Service (C3S) repository https://cds.climate.copernicus.eu/datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy conception: JH, NA, CB. Statistical methodology: JH, NA, CB. Identification and collation of data: JH, CB. Wrote the first draft of the manuscript: JH, NA. All authors critically edited the first draft. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to the Infectious Diseases Data Observatory (IDDO) for its scientific and technical support in developing this study. We thank Dr Prabin Dahal, Dr Makoto Saito, Dr James Wilson and James Watson for their expert guidance on statistical modelling and their valuable feedback throughout the analysis.\u003c/p\u003e\n\u003cp\u003eWe also extend our deep appreciation to Dr Nancy Saravia at the Centro Internacional de Entrenamiento e Investigaciones Médicas (CIDEIM) for their critical mentorship and ongoing support in strengthening knowledge in tropical neglected diseases research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eCentro Internacional de Entrenamiento e Investigaciones Médicas (CIDEIM).\u0026nbsp;Calle 18 # 122-135, Campus Universidad ICESI, Edificio O, Cali Colombia\u003cbr\u003e\u003cstrong\u003eJuan Sebastian Hurtado Zapata –\u0026nbsp;\u003c/strong\[email protected]\u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u0026amp; Neal Alexander –\u0026nbsp;\u003c/strong\[email protected]\u003cbr\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eUniversidad Icesi, Calle 18 # 122-135, Cali Colombia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeal Alexander -\u0026nbsp;\u003c/strong\[email protected] \u003csup\u003e\u003cbr\u003e\u0026nbsp;3.\u0026nbsp;\u003c/sup\u003eAlliance of Bioversity International and the International Center for Tropical Agriculture (CIAT). Km 17, Cali-Palmira Highway, Cali (Palmira), Valle del Cauca, Colombia, 763537\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCamilo Barrios Perez\u003c/strong\u003e -\u0026nbsp;[email protected]\u003cbr\u003e\u003csup\u003e4\u0026nbsp;\u003c/sup\u003eField Epidemiology Training Program (FETP), Instituto Nacional de Salud de Colombia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJuan Sebastian Hurtado Zapata\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e–\u0026nbsp;\u003c/strong\[email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. World Malaria Report 2023: addressing inequity in the global malaria response [Internet]. Geneva: World Health Organization. 2023 [cited 2025 Feb 17]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789240104440\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789240104440\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedraza Calder\u0026oacute;n J, Informe de Evento. 2023. Malaria [Internet]. Bogota, D.C; 2022 Jan. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ins.gov.co/buscador-eventos/Informesdeevento/MALARIA%20INFORME%20DE%20EVENTO%202023.pdf\u003c/span\u003e\u003cspan address=\"https://www.ins.gov.co/buscador-eventos/Informesdeevento/MALARIA%20INFORME%20DE%20EVENTO%202023.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontoya-Lerma J, Solarte YA, Giraldo-Calder\u0026oacute;n GI, Qui\u0026ntilde;ones ML, Ruiz-L\u0026oacute;pez F, Wilkerson RC, et al. Malaria vector species in Colombia-A review HHS Public Access Author manuscript. Volume 106. Mem Inst Oswaldo Cruz; 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePadilla-Rodr\u0026iacute;guez JC, Olivera MJ, Ahumada-Franco ML, Paredes-Medina AE. Malaria risk stratification in Colombia 2010 to 2019. PLoS ONE. 2021;16(3 March).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoveda G, Graham N, Epstein P, Rojas W, Qui\u0026ntilde;ones M, V\u0026eacute;lez ID, et al. Climate and ENSO Variability Associated with Vector-Borne Diseases in Colombia. El Nino and the Southern Oscillation. Cambridge: Cambridge University Press; 2010. pp. 183\u0026ndash;204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePadilla-Rodr\u0026iacute;guez JC, Olivera MJ, Ahumada-Franco ML, Paredes-Medina AE. Malaria risk stratification in Colombia 2010 to 2019. PLoS ONE. 2021;16(3 March).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLozada-Martinez YA, Orozco-Chinome ID, Molina-Franky JE, Acevedo-Lopez J, Acevedo-Lopez D, Bola\u0026ntilde;o-Romero N et al. Relationship between Duffy Genotype/Phenotype and Prevalence of Plasmodium vivax Infection: A Systematic Review. Trop Med Infect Dis [Internet]. 2023 Sep 30 [cited 2025 Feb 28];8:463\u0026ndash;75. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/tropicalmed8100463\u003c/span\u003e\u003cspan address=\"10.3390/tropicalmed8100463\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardona-Arias JA, Higuita-Guti\u0026eacute;rrez LF, Carmona-Fonseca J. Social determination of malaria in pregnancy in Colombia: a critical ethnographic study. Malar J. 2023;22(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeged-Rivadeneira A, \u0026Aacute;ngel A, Gonz\u0026aacute;lez-Casabianca F, Rivera C. Malaria intensity in Colombia by regions and populations. PLoS ONE. 2018;13(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParham PE, Michael E. Modeling the effects of weather and climate change on malaria transmission. Environ Health Perspect. 2010;118(5):620\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartens WJM, Jetten TH, Rotmans J, Niessen LW. Climate change and vector-borne diseases: A global modelling perspective. Glob Environ Change. 1995;5(3):195\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez V\u0026aacute;squez C, Barrera Escobar S, Tob\u0026oacute;n-Casta\u0026ntilde;o A. Low frequency of asymptomatic and submicroscopic plasmodial infections in Urab\u0026aacute; Region in Colombia. J Trop Med. 2018;2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnston GL, Smith DL, Fidock DA. Malaria\u0026rsquo;s Missing Number: Calculating the Human Component of R0 by a Within-Host Mechanistic Model of Plasmodium falciparum Infection and Transmission. PLoS Comput Biol. 2013;9(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite NJ, Pukrittayakamee S, Hien TT, Faiz MA, Mokuolu OA, Dondorp AM. Malaria. Vol. 383, The Lancet. Elsevier B.V.; 2014. pp. 723\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGostic KM, McGough L, Baskerville EB, Abbott S, Joshi K, Tedijanto C et al. Practical considerations for measuring the effective reproductive number, Rt [Internet]. 2020. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://medrxiv.org/lookup/doi/\u003c/span\u003e\u003cspan address=\"http://medrxiv.org/lookup/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2020.06.18.20134858\u003c/span\u003e\u003cspan address=\"10.1101/2020.06.18.20134858\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCori A, Ferguson NM, Fraser C, Cauchemez S. A new framework and software to estimate time-varying reproduction numbers during epidemics. Am J Epidemiol. 2013;178(9):1505\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNash R, Nouvellet P, Cori A. Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges. PLOS Digit Health. 2022;1:e0000052.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNash RK, Nouvellet P, Cori A. Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges. PLOS Digit Health. 2022;1(6 June).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAranda DFC. Quantification with SPEI of historical Droughts and under probable climatic change in the climatological station Zacatecas, Mexico. Tecnologia y Ciencias del Agua. 2018;9(2):210\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng L, Sheffield J, Wei Z, Ek M, Wood EF. An enhanced Standardized Precipitation-Evapotranspiration Index (SPEI) drought-monitoring method integrating land surface characteristics. Earth Sys Dyn. 2024;15(5):1277\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaves LF, Pascual M. Comparing models for early warning systems of neglected tropical diseases. PLoS Negl Trop Dis. 2007;1(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarmona-Fonseca J, Olivera MJ, Yasnot-Acosta MF. A Retrospective Review on Severe Malaria in Colombia, 2007\u0026ndash;2020. Pathogens. 2022;11(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoutledge I, Chev\u0026eacute;z JER, Cucunub\u0026aacute; ZM, Rodriguez MG, Guinovart C, Gustafson KB et al. Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting. Nat Commun. 2018;9(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmarilis Hurtado L, Calzada JE, Rigg CA, Castillo M, Fernando Chaves L. Climatic fluctuations and malaria transmission dynamics, prior to elimination, in Guna Yala, Rep\u0026uacute;blica de Panam\u0026aacute;. Malar J [Internet]. 2018 [cited 2025 Mar 1];17:85. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-018-2235-3\u003c/span\u003e\u003cspan address=\"10.1186/s12936-018-2235-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePriestley CHB. On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCristea NC, Kampf SK, Burges SJ. Revised Coefficients for Priestley-Taylor and Makkink-Hansen Equations for Estimating Daily Reference Evapotranspiration. J Hydrol Eng. 2013;18(10):1289\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuleiman AA, Hoogenboom G. Comparison of Priestley-Taylor and FAO-56 Penman-Monteith for Daily Reference Evapotranspiration Estimation in Georgia. Available from: www.Georgiaweather.net.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePereira LS. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 [Internet]. 2015. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/235704197\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/235704197\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaslinger K, Sch\u0026ouml;ner W, Anders I. Future drought probabilities in the Greater Alpine Region based on COSMO-CLM experiments - Spatial patterns and driving forces. Meteorol Z. 2016;25(2):137\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStagge JH, Tallaksen LM, Xu CY. Standardized precipitation-evapotranspiration index (SPEI): Sensitivity to potential evapotranspiration model and parameters [Internet]. 2014. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/270576580\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/270576580\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee S, Moriasi DN, Danandeh Mehr A, Mirchi A. Sensitivity of Standardized Precipitation and Evapotranspiration Index (SPEI) to the choice of SPEI probability distribution and evapotranspiration method. J Hydrol Reg Stud. 2024;53:101761.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBortman M. Elaboraci\u0026oacute;n de corredores o canales end\u0026eacute;micos mediante planillas de c\u0026aacute;lculo.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuber JH, Johnston GL, Greenhouse B, Smith DL, Perkins TA. Quantitative, model-based estimates of variability in the generation and serial intervals of Plasmodium falciparum malaria. Malar J. 2016;15(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSt. Laurent B. Mosquito vector diversity and malaria transmission. Front Malar. 2025;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLerma-Montoya J, Solarte Y, Giraldo-Calder\u0026oacute;n G, Qui\u0026ntilde;ones M, Ruiz-L\u0026oacute;pez FR, Wilkerson RC et al. Malaria vector species in Colombia - A review. Mem Inst Oswaldo Cruz [Internet]. 2011 Jul 7 [cited 2025 May 12];106(I):223\u0026ndash;38. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scielo.br/j/mioc/a/p65fN6bQLnZLdS3M9K83djt/?lang=en\u003c/span\u003e\u003cspan address=\"https://www.scielo.br/j/mioc/a/p65fN6bQLnZLdS3M9K83djt/?lang=en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiedrahita S, Correa MM. Malaria Vectors and Plasmodium Transmission in Malaria-Endemic Localities of Colombia. Trop Med Infect Dis. 2024;9(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaminade C, Kovats S, Rocklov J, Tompkins AM, Morse AP, Col\u0026oacute;n-Gonz\u0026aacute;lez FJ et al. Impact Clim change global Malar distribution. 2014;111(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKibret S, Wilson GG, Ryder D, Tekie H, Petros B. Malaria impact of large dams at different eco-epidemiological settings in Ethiopia. Trop Med Health. 2017;45(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConde M, Pareja PX, Orjuela LI, Ahumada ML, Dur\u0026aacute;n S, Jara JA et al. Larval habitat characteristics of the main malaria vectors in the most endemic regions of Colombia: Potential implications for larval control. Malar J. 2015;14(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinka ME, Rubio-Palis Y, Manguin S, Patil AP, Temperley WH, Gething PW et al. The dominant Anopheles vectors of human malaria in the Americas: Occurrence data, distribution maps and bionomic pr\u0026eacute;cis. Parasit Vectors. 2010;3(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaaijmans KP, Blanford S, Bell AS, Blanford JI, Read AF, Thomas MB. Influence of climate on malaria transmission depends on daily temperature variation. Proc Natl Acad Sci U S A. 2010;107(34):15135\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarrasquilla MC, Gonz\u0026aacute;lez R, Pineda SR, Ocampo CB, Montoya-Lerma J, Pareja-Loaiza P et al. Ecoregional and altitudinal distribution of the principal malaria vectors in Colombia.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaves LF, Pascual M. Comparing Models for Early Warning Systems of Neglected Tropical Diseases. [cited 2025 Feb 28]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cdc\u003c/span\u003e\u003cspan address=\"http://www.cdc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria, Climate variability, Effective reproduction number (Rt), SPEI, Temperature, Colombia","lastPublishedDoi":"10.21203/rs.3.rs-8643526/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8643526/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn Colombia and elsewhere, malaria transmission is highly sensitive to climate. The Standardized Precipitation-Evapotranspiration Index (SPEI) is used in agriculture to schedule crop planting and harvesting. The distribution and spread of malaria vectors are influenced by climatic factors, including humidity, temperature, and precipitation. The occurrence and distribution of water sources influence mosquito reproduction and transmission capacity, as well as human exposure to infectious vectors. This study evaluates the association between i) transmission of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e and \u003cem\u003evivax\u003c/em\u003e, represented by the time-varying effective reproductive number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e), and ii) climate variables \u0026mdash; specifically SPEI and temperature \u0026mdash; in Colombia from 2014 to 2023.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMalaria surveillance data from the Colombian Public Health Surveillance System (SIVIGILA) were analyzed alongside climate data for four malaria-endemic administrative units (departments): Antioquia, Cauca, Choc\u0026oacute; and Nari\u0026ntilde;o. \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e was estimated by \u003cem\u003ePlasmodium\u003c/em\u003e species using a mechanistic framework informed by regional vector ecology and parasite dynamics. Associations between \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e and climate variables, including lagged effects (1\u0026ndash;3 months), were assessed using generalized additive models (GAMs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 476,810 malaria cases were reported from 2014 to 2023, with 63.2% due to \u003cem\u003eP. falciparum\u003c/em\u003e and 36.8% to P. vivax. In Choc\u0026oacute;, generalized additive models showed a strong positive association with temperature. Together with \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e, temperature explained 45.6% of the deviance for \u003cem\u003eP. falciparum\u003c/em\u003e in Choc\u0026oacute; (R\u0026sup2; = 0.381, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In Antioquia, lower SPEI values (drier conditions) were associated with increased transmission, explaining 49.6% of the deviance for P. vivax (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In Nari\u0026ntilde;o and Cauca, temperature was associated with \u003cem\u003eP. falciparum incidence\u003c/em\u003e, explaining 45%-50% of the deviance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e values often rose 1\u0026ndash;3 months prior to increases in malaria incidence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTemperature was moderately associated with malaria transmission in the study area, particularly the Colombian Pacific Coast, specifically for \u003cem\u003eP. falciparum\u003c/em\u003e, while both temperature and SPEI were associated with transmission of both \u003cem\u003ePlasmodium\u003c/em\u003e species in Antioquia. These findings support integrating climate-informed surveillance indicators to enhance public health preparedness.\u003c/p\u003e","manuscriptTitle":"Malaria–climate interface in the Pacific and Andean regions of Colombia between 2014 and 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:58:52","doi":"10.21203/rs.3.rs-8643526/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-16T15:36:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T04:09:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-28T17:40:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176050043617869598252580177807721672517","date":"2026-02-18T18:33:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221885311531657332974913307258711428732","date":"2026-02-01T03:44:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-27T17:08:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-21T15:49:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-20T18:06:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2026-01-20T00:29:32+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":"7cca3ab9-45f7-4677-abb9-e6ed86894fd1","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T15:40:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:58:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8643526","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8643526","identity":"rs-8643526","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}