Predicting mosquito populations before a surge - Albuquerque Journal

Predicting mosquito populations before a surge

New Mexico is in monsoon season, which brings wetter – and, thankfully, cooler – days and fills our reservoirs. But it also brings the potential for floods, which in turn allows the most dangerous animal in the world – mosquitoes – to thrive.

Mosquitoes cause millions of deaths globally every year by spreading diseases. In New Mexico, mosquitoes carry West Nile virus. In other parts of the world, such mosquito-borne diseases as malaria, chikungunya, Zika virus and others wreak havoc on communities.

Knowing where an increased population will arise could help prevent the spread of these diseases by providing public health departments with advance warning to respond to and prepare for potential outbreaks.

At Los Alamos National Laboratory, we’re studying mosquito populations to understand how they grow, how they change with the seasons and, in particular, how they spread infectious diseases to humans and other animals. The goal is to create a computer-based model that will accurately simulate mosquito populations based on data about precipitation, temperature, water levels and other environmental factors in a given area, so people will know ahead of time about an increased risk of disease transmission.

For this project, we’re looking specifically at West Nile virus, which birds transmit to humans via mosquitoes. We analyzed 15 years of data from several different locations in the United States and Canada, making it one of the largest modeling studies of mosquito populations over time ever conducted. Previous studies have looked at temperature and precipitation, but this is the first to use stream-gauge and water-level data.

Because standing water levels directly influence mosquito populations, this data turned out to be a crucial missing link in predictive models. Using this information, we trained the models on six years of existing data and then had the models predict the next several years of historical data as if they did not yet exist.

To train a model, we had to make some assumptions about mosquito life cycles and how they respond to such environmental variables as temperature, precipitation and water levels. For example, juvenile mosquito stage-development rates were assumed to depend on temperature. Available standing water levels were assumed to affect habitat for eggs and larva, with reduced standing water resulting in fewer successfully hatching eggs and more competition between larva. We also assumed that temperature affects the adult mosquito lifespan.

From those assumptions, we then looked at what the models predicted and compared them to the actual data.

The results were surprising: the predictive models very closely resembled the actual mosquito population patterns. This is promising news. If the model can accurately predict mosquito populations before they surge, public health officials could get an early warning that their communities might be at greater risk for the spread of mosquito-borne diseases. This can give them time to put in place such preventive measures as spraying where mosquitoes are laying eggs and where they are seeking hosts, distributing personal protective measures, including repellent or nets, and draining areas of standing water. Larvicide can be put into water bodies with mosquito larva. The models provide a kind of early warning system that enables action to significantly reduce disease transmission.

This is particularly important given rising water levels, warming temperatures and more extreme precipitation events around the world – not just in the aftermath of hurricanes – which could mean even more widespread mosquito-borne diseases.

The potential to reduce the spread of these diseases is about more than keeping individual communities healthy; it also has far-reaching national and global security implications. Regions hit hard by illness are more vulnerable to economic woes, stressed health care systems, and other destabilizing conditions that can weaken the social fabric of a community and foster political unrest.

While this might be truer in developing countries with less reliable supports and infrastructure, developed countries such as the United States also face the very real threat of rapid, unpredictable disease outbreaks – as we now know all too well. Missed work and medical costs all tax the economy, with lasting ripple effects. Predicting mosquito populations is one way to help mitigate those impacts.

Our modeling research also deepens our understanding of how diseases spread – which is critical to protecting U.S. citizens at home and abroad, as well as war-fighters deployed overseas. Introduction of a new disease to a country or community, or even a rapidly spreading existing disease, all pose risks. Knowing these patterns ahead of time will help officials develop the proper response and keep people safer.

Carrie Manore is a scientist at Los Alamos National Laboratory. Her research focuses on modeling infectious disease spread and data analytics. A version of this article first appeared in “Scientific American.”


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