This study explores Mpox transmission dynamics using a mathematical and data-driven epidemiological model that incorporates two viral strains, Clade I and Clade II. The model includes transmission pathways between humans and mammals and divides the human population into susceptible, exposed, infectious, hospitalized, and recovered groups. Weekly data from the WHO for Spain, Italy, Nigeria, and the DRC from 2022 to 2024 are used for model validation via non-linear least-squares fitting, with model performance assessed by Root Mean Squared Error (RMSE). We conduct time-series analysis to detect trends and anomalies in Mpox cases, with scenario simulations examining strain-specific transmission and the basic reproduction number (${\mathcal{R}}_0)$. The mathematical model fit is compared with two statistical model fits to emphasize the importance of developing a model that incorporates Mpox strain. Mathematical analysis confirms the model’s key properties, including positivity, boundedness, and equilibrium stability. Results underscore the importance of strain-specific dynamics and varying infection proportions for ${\mathcal{R}}_0$. This study combines mathematical rigor with empirical data to provide valuable insights into Mpox transmission and offers a framework for understanding multi-strain pathogens in diverse populations. Results from the simulation indicate that an increase in the effective contact rate leads to the dominance of the prevalent Mpox Clades in each country. Based on these findings, we recommend the implementation of strategies aimed at reducing the effective contact rate to control the spread of the virus strains.

Background: Malaria is a critical public health concern in Nigeria with the country bearing an uneven burden of the disease. In Nigeria, malaria is one of the main causes of child mortality and despite all efforts to reduce malaria mortality rates, the disease remains a major concern, notably among children under‐fives. There is a paucity of data on more localized predictive malaria risk geospatial maps to inform control and elimination strategies amidst limited public health resources in this setting. This modelling study therefore sought to understand, predict and map malaria risk in the presence of environmental factors in Nigeria. Methods: This study utilized data from the 2021 Nigeria Malaria Indicator Survey (NMIS), conducted under the Demographic and Health Surveys (DHS) Program. The 2021 NMIS marks the third malaria indicator survey carried out in Nigeria, following previous surveys in 2010 and 2015. The outcome variable of interest is the number of individuals in each sampled cluster who tested positive on the rapid diagnostic test (RDT). This study investigated spatial risk factors for malaria prevalence in Nigeria through geostatistical modelling approaches. The implementation of the models was carried out with the integrated nested Laplace approximation (INLA) method via the stochastic partial differential equation (SPDE) approach in R‐INLA. Results: The study identified aridity (log‐odds = −0.0400, 95% CrI = −0.0610, −0.0190) and enhanced vegetation index (log‐odds = 9.3930, 95% CrI = 7.4220, 11.3760) as significant predictors of under‐five malaria risk. The fitted Bayesian geospatial spatial model with covariates predicted malaria prevalence of 24.9% with a range of 0.5% to 74.2%. The predicted malaria prevalence was highest in parts of Zamfara (prevalence > 70%) and Kebbi (prevalence > 60%) States. Conclusion: These findings are of the utmost significance for policymakers involved in malaria control and elimination efforts. They provide evidence‐based information that can guide resource mobilization and targeting, intervention design, and monitoring strategies. The identification of environmental predictors like aridity and enhanced vegetation index suggests the need for location‐specific interventions according to environmental determinants of malaria transmission.