Spatial fine-resolution model of malaria risk for the Colombian Pacific region

Time lag effect on malaria transmission dynamics in an Amazonian Colombian municipality and importance for early warning systems

Malaria remains a significant public health problem worldwide, particularly in low-income regions with limited access to healthcare. Despite the use of antimalarial drugs, transmission remains an issue in Colombia, especially among indigenous populations in remote areas. In this study, we used an SIR Ross MacDonald model that considered land use change, temperature, and precipitation to analyze eco epidemiological parameters and the impact of time lags on malaria transmission in La Pedrera-Amazonas municipality. We found changes in land use between 2007 and 2020, with increases in forested areas, urban infrastructure and water edges resulting in a constant increase in mosquito carrying capacity. Temperature and precipitation variables exhibited a fluctuating pattern that corresponded to rainy and dry seasons, respectively and a marked influence of the El Niño climatic phenomenon. Our findings suggest that elevated precipitation and temperature increase malaria infection risk in the following 2 months. The risk is influenced by the secondary vegetation and urban infrastructure near primary forest formation or water body edges. These results may help public health officials and policymakers develop effective malaria control strategies by monitoring precipitation, temperature, and land use variables to flag high-risk areas and critical periods, considering the time lag effect.

Environmental and socio-economic determinants of the occurrence of malaria clusters in Colombia

This study identifies the environmental and socio-economic determinants of clusters of high malaria incidence in Colombia during the period of 2008-2019. The malaria cases were obtained from the National System of Surveillance in Public Health, with 798,897 cases reported in the 986 Colombian municipalities evaluated during the study period. Spatial autocorrelation of incidence was examined with global and local indices. Clusters were identified in the Amazon, Pacific, and Uraba-Bajo Cauca-Alto Sinú regions. The factors associated with a municipality belonging to a high-incidence cluster were identified using a logistic regression model with mixed effects and showed a positive association for the variables (forest coverage and minimum multi-year average rainfall). An inverse relationship was observed for aqueduct coverage and the odds of belonging to a cluster. A 1% increase in forest coverage was associated with a 4.2% increase in the odds of belonging to a malaria cluster. The association with minimum multi-year average rainfall was positive (OR = 1.0011; 95% CI 1.0005-1.0027). A 1% increase in aqueduct coverage was associated with a 4.3% decrease in the odds of belonging to malaria cluster. The identification of malaria cluster determinants in Colombia could help guide surveillance and disease control policies.

Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique

Mozambique is a country in Southern Africa with around 30 million inhabitants. Malaria is the leading cause of mortality in the country. According to the WHO, Mozambique has the third highest number of malaria cases in the world, representing approximately 5% of the world total cases. Sussundenga District has the highest incidence in the Manica province and environmental conditions are the major contributor to malaria transmission. There is a lack of malaria risk maps to inform transmission dynamics in Sussundenga village. This study develops a malaria risk map for Sussundenga Village in Mozambique and identifies high risk areas to inform on appropriate malaria control and eradication efforts. One hundred houses were randomly sampled and tested for malaria in Sussundenga Rural Municipality. To construct the map, a spatial conceptual model was used to estimate risk areas using ten environmental and anthropic factors. Data from Worldclim, 30 × 30 Landsat images were used, and layers were produced in a raster data set. Layers between class values were compared by assigning numerical values to the classes within each layer of the map with equal rank. Data set input was classified, using diverse weights depending on their appropriateness. The reclassified data outputs were combined after reclassification. The map indicated a high risk for malaria in the northeast and southeast, that is, the neighborhoods of Nhamazara, Nhamarenza, and Unidade. The central eastern areas, that is, 25 de Junho, 1 and 2, 7 de Abril, and Chicueu presented a moderate risk. In Sussundenga village there was 92% moderate and 8% high risk. High malaria risk areas are most often located in densely populated areas and areas close to water bodies. The relevant findings of this study can inform on effective malaria interventions.