MS09 - MEPI-07 Part 3 of 3

Recent Trends in Mathematics of Vector-borne Diseases and Control (Part 3)

Friday, July 18 at 4:00pm

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Note: this minisymposia has multiple sessions. The other sessions are: Part-1 and Part-2.

Abba Gumel (University of Maryland), Alex Safsten, Arnaja Mitra (both University of Maryland)

Description:

Vector-borne diseases, such as malaria, dengue, Lyme disease, leishmaniasis, and West Nile virus, constitute over 17% of all infectious diseases of humans, with malaria (which causes in excess of 600,000 deaths annually, mostly in children under the age of five) being the most important of these diseases. These vectors are typically controlled by using insecticide-based control measures, and their lifecycle (and those of the pathogens they vector) are greatly affected by changes in local climatic conditions, such as temperature, precipitation, and humidity. This minisymposium brings together researchers to discuss the recent advances in modeling the spread and control of vector-borne diseases. Some of the topics to be discussed include whether or not the recent quest to eradicate malaria is feasible using currently-available insecticides-based control resources, assessing the impact of insecticide and drug resistance on vector population abundance and the intensity of the disease they cause, assessing the potential for alternative biocontrol measures (such as sterile insect technique and the use of Wolbachia infection-based measures and the release of gene drives, such as CRISPR-Cas9) to control vector species, assessing the impact of climate change on the distribution and abundance of vector species etc.

Casey O'Brien

North Carolina State University

"Modeling a Novel Gene Drive That Targets Immune Responses"

Gene drive technologies hold promise for controlling invasive pests, mitigating disease transmission, and protecting local ecosystems and agriculture. However, their deployment hinges on resolving safety concerns, particularly the risk of unintended spread into non-target populations. Current confinement strategies rely largely on invasion thresholds which take advantage of unstable equilibrium points in allele frequency, below which the drive will not spread. This maintains local confinement by preventing migrants from spreading the drive in surrounding populations. While this is an effective strategy for gene drives meant to introduce a trait to a population, its success has been more limited in suppression gene drives. We circumvent this issue by designing a novel suppression drive system that targets the immune response of an organism to a local stressor (i.e., endemic virus, fungus, or a specialized parasitoid). The drive system increases the target organism’s susceptibility to the stressor by increasing the likelihood of acquiring the infection or the impact of infection on the organism. This means that the drive system’s fitness cost is dependent on the abundance of the stressor. We model several drive systems to consider the efficacy of the system in different settings

Jackson Champer

Peking University

"Suppression gene drive for mosquito control: large scale spatial models and impact on disease transmission"

Gene drive alleles bias inheritance in the favor, allowing them to quickly spread throughout a population. They could combat disease by rapidly spreading a cargo gene that blocks pathogen transmission, or they could directly suppress vector populations. Progress has been made to reduce resistance allele formation, a main obstacle to successful gene drive in Anopheles stephensi mosquitoes, yielding efficient systems. However, computational analysis using individual-based models predicts that suppression drives may still not succeed in spatially structured natural populations due to the 'chasing' phenomenon that causes chaotic, long-term persistence of both drive and wild-type alleles. To assess this effect on malaria transmission, we developed a deep-learning model to allow assessment of many drive, ecology, and disease parameters without a large computational burden. We found that malaria could potentially be eliminated even if the mosquito population persists. Thus, despite unexpected complexity, gene drive remains a potentially powerful method to reduce malaria infections.

Daihai He

The Hong Kong Polytechnic University

"Resolving the enigma of Iquitos and Manaus: A modeling analysis of multiple COVID-19 epidemic waves in two Amazonian cities"

The nearby cities of Iquitos (Peru) and Manaus (Brazil) experienced the world’s highest infection and mortality rates during the first COVID-19 wave in 2020. Key studies suggested that >70% of the city populations were infected in this wave and thus close to herd immunity and protected. It remains an enigma as to why a deadly second wave followed in Manaus worse than the first. To resolve this, we present a data-driven model of epidemic dynamics in Iquitos which we use to help explain and model events in Manaus. The partially observed Markov process model simultaneous fits a flexible “variable R0”, estimates long-term immunity waning and impulsive immune evasion, and thus provides a comprehensive framework for characterizing and modeling new variants of concern.

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Annual Meeting for the Society for Mathematical Biology, 2025.