Abstract
The causative agent of cholera, Vibrio cholerae, is a bacterium native to the aquatic environment and commensal to zooplankton, namely copepods. V. cholerae thrives in warm, moderately saline water and its incidence is strongly influenced by environmental factors, which have proven critical for predictive awareness of cholera by identifying outbreak locations and timing. Susceptible-Infected-Recovered (SIR) models provide useful information for understanding transmission dynamics and epidemic curves of disease outbreaks. Previous such models lacked predictive ability due to limited data in regions where cholera persists. Here, we include climate variability parameters derived from currently available remote sensing data as primary input, allowing greater utility, compared to traditional SIR models. We present models for two African countries where cholera is endemic, Democratic Republic of Congo (DRC) (R (2) = 0.769) and Nigeria (R (2) = 0.756), that incorporate data for temperature, precipitation, and drought index and have been calibrated using weekly cholera case data from 2017 to 2019. Results suggest these models can be used for reasonably accurate retrospective analyses at both country-wide scale for which they were calibrated and modified for smaller spatial extent, including cholera outbreaks in Borno State, Nigeria and North Kivu, DRC. However, results also suggest predicting future epidemic transmission will be challenging due to data limitations in case reporting and intervention strategies. Thus, climate factors should be considered for future SIR modeling efforts, but further advances in data collection are required for these SIR models to become viable predictive tools.