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ARS Home » Research » Research Project #443641

Research Project: Epidemiological Modeling of Vector-borne Diseases

Location: Foreign Arthropod Borne Animal Disease Research

Project Number: 3022-32000-062-008-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Jul 1, 2023
End Date: Jul 31, 2025

Insect vectors such as mosquitoes and biting flies transmit pathogens known to cause a wide range of diseases such as vesicular stomatitis, Japanese encephalitis, and Rift Valley fever. In many countries, mosquito control measures are implemented to reduce the spread of these diseases. These measures include larval site reduction, biological control, genetic control and the use of insecticides, barriers, and larvicides, among others. However, it can be challenging to determine the most effective insect control measure in a particular setting. This is where simulation can aid operations. By creating a model of the vector population and the environment in which they live, different control measures can be tested to see which is the most effective at reducing the vector population numbers and ultimately, the transmission of disease. One objective of this project is to create an interactive software that can simulate the dynamics of vector populations under various control methods. The software allows the user to input the relevant data regarding the insect life cycle, such as development periods, and to define the interaction of different vector populations. It also accepts information about the environment, for instance the number of larval sites and their spatial distribution. After defining the vector and its environment, the user will be able to run simulations evaluating the efficacy of various management methods and assess their effectiveness. Since the vector lifecycle parameters are temperature dependent, the simulation uses temperature data which can either be provided by the user or accessed from a database used by the software. In addition to assessing the control measures, the software can be used to simulate the diffusion of insects, and transmission of disease to human or other hosts. To this end, the user can define the transmission model and diffusion mechanisms, add other layers of data such as host population distribution and simulate the system. To make the software user friendly, there are predefined transmission models, mosquito and other insect diffusion mechanisms in addition to database including human population density and temperature for different locations. This enables the user to simulate the system with minimal inputs. The second objective is to develop accurate network-based transmission models. Such models not only improve vector-borne disease transmission models but also advance the modeling of direct virus transmission among individuals such as with monkey pox, vesicular stomatitis virus, or some routes of pig-to-pig Japanese encephalitis virus transmission. Traditionally, virus transmission models have been analyzed using ordinary differential equations that assume homogeneous mixing of populations. However, in real-world scenarios, the interactions among different components of the transmission system are heterogeneous. While there are mean field network models that consider heterogeneity in transmission components, due to the stochastic nature of transmissions, such models cannot accurately describe real-world transmission.

Simplifying transmission models can result in overestimating the course of an epidemic when compared to the exact simulation of the process. In fact, we have witnessed such overestimations in the case of COVID spread. As individuals decrease the number of contacts, the viable paths for virus transmission change, leading to discrepancies between the outputs of transmission models that do not account for the sparse nature of contact networks. From a modeling perspective, the mitigation measures taken to prevent a spreading disease may result in a sparse contact network such as at the onset or ending of an outbreak, which necessitates an accurate network-based transmission model. Additionally, stochastic network-based models can be helpful in describing the interaction and diffusion of vectors in different regions. These models take into account the randomness and variability inherent in biological systems and can provide a more accurate representation of the spread of a disease. Unfortunately, exact mathematical analysis of stochastic network-based models is not tractable, and mean-field approximation is applied to perform the analysis, which incurs a large discrepancy between reality and results. To overcome this problem, we seek to develop new moment closure techniques in combination with a group state model to advance the approximations describing the stochastic network-based spreading models. This is particularly pivotal in applications where we want to estimate model parameters using observed data. Additionally, we will apply these approximating techniques to the simulation engine of the software we described before. This decreases the software running time required for the simulation of virus transmission and vector diffusion in large populations without sacrificing accuracy.