Forecasting the mosquito population in the Kaluga Region using a time series analysis

The purpose of the research is to develop a seasonal forecasting model for mosquito populations (family Culicidae) in the Kaluga Region based on time series analysis methods with climatic factors taken into account, which ensures a forecast accuracy of at least 85% to plan proactive anti-epidemic measures. Materials and methods . The study on mosquito population dynamics in the Kaluga Region for 2014–2024 used an integrated approach combining field observations, statistical analysis, and mathematical modeling. Climatic parameters (average monthly temperatures, precipitation, and air humidity) were analyzed in parallel. A time series modeling method was used. Results and discussion. The forecasting model for mosquito populations in the Kaluga Region for 2024 was developed using the seasonal SARIMA model. The results obtained demonstrate the forecast reliability: the mean absolute percentage error is 7.9%, which means the forecast deviates from actual values by less than 8%, on average. The standard error of 147.2 specimens indicates that the forecast data may differ in absolute values from actual values by approximately 150 specimens, with an average population size of approximately 1,800–2,500 specimens. These values demonstrate the high model reliability and applicability for practical use. These results have important practical implications for planning timely treatments of areas, optimizing epidemiological surveillance, allocating resources to control disease vectors, and informing the public about periods of increased mosquito activity.