Predicting Infection Risk of Mosquito-borne Disease

Malaria, which is transmitted by mosquitoes, remains one of the greatest threats to global health, infecting more people than ever before. Understanding how the risk of catching a mosquito-borne infection varies depending on the environment is an important step in planning and implementing effective control measures. The rate at which humans become infected is determined by how often they are bitten by mosquitoes and the proportion of mosquitoes that are infectious. It is often assumed that if the percentage of infectious mosquitoes increases, so will the rate at which humans get bitten. But in a new study published in the open access journal PLoS Biology, David Smith, Jonathan Dushoff, and F. Ellis McKenzie challenge this assumption. Using a mathematical model, the authors show that the rate at which humans are bitten and the proportion of infectious mosquitoes peak at different times and places, revealing that the standard metric of estimating the risk of infection -- the average number of times an infectious mosquito bites a person per day -- is flawed.

The distribution of humans and suitable habitat for mosquito larvae varies across the landscape. And the density of mosquito populations varies seasonally, rising and falling with changes in rainfall, temperature, and humidity. Temporal and spatial variations in mosquito populations affect the rate humans get bitten, the number of infectious mosquitoes, and the risk of infection. The mathematical model that Smith and colleagues developed predicts that human biting rate is highest shortly after mosquito population density peaks, typically either near breeding sites or where human density is highest. The proportion of infectious mosquitoes, on the other hand, reflects the age of the mosquito population: it peaks where older mosquitoes are found -- farther from breeding sites -- and when populations are declining. The combination of these factors results in, for example, the surprising prediction that the risk of infection can be lowest just outside an edge of town.

By mapping larval habitats against the local risk of mosquito-borne infections, Smith and colleagues conclude, epidemiological models can be developed to predict risk for local populations. Their results make the case that mathematical models can help public health officials calculate risk of infectious diseases in heterogeneous environments -- that is, real world conditions -- when vector ecology and the parameters of transmission are well characterized. Any plan to prevent and control the spread of mosquito-born infections would clearly benefit from paying attention to mosquito demography and behavior.

Source: Public Library of Science