# Preliminary Study on the Development of a Transmission Model for Canine Distemper Virus in Wildlife Populations Using Heat Mapping and the Basic Reproduction Number

## TL;DR

This paper develops a model to predict Canine Distemper Virus transmission in wildlife using heat maps and R0 to identify high-risk areas for conservation efforts.

## Contribution

The study introduces a novel modeling approach combining heat mapping and R0 to estimate CDV transmission in wildlife populations.

## Key findings

- Environmental factors and animal density hotspots increase CDV transmission risk.
- High-risk zones overlap with wildlife movement corridors and human interface areas.
- The model supports targeted monitoring and conservation strategies for CDV outbreaks.

## Abstract

Canine Distemper Virus (CDV) is a contagious disease that affects many wildlife species and threatens biodiversity. However, viral spread in wildlife, especially in tropical areas, is not well understood due to limited data and difficulties in monitoring wild animals. This study developed a model to estimate the CDV transmission using spatial heat maps and basic reproduction number (R0) in which field observations, environmental data and reported CDV cases were used to predict areas with higher transmission risk. The results showed that environmental factors, animal density and areas where wildlife and humans interact increase the risk of CDV transmission. This model is useful as a preliminary tool to identify high-risk zones and support targeted monitoring, early detection and evidence-based conservation efforts.

Canine Distemper Virus (CDV) is a highly contagious disease that affects a wide range of wildlife species, posing a serious threat to biodiversity and conservation efforts. Despite its ecological significance, the transmission dynamics of CDV in wildlife remain poorly understood, especially in tropical ecosystems. One of the main challenges in studying CDV transmission is the lack of reliable epidemiological data and the difficulty in capturing and monitoring wild animals for surveillance purposes. Thus, this study aims to develop a model to estimate the potential transmission of CDV in wildlife populations using spatial heat mapping and the basic reproduction number (R0) as key indicators. A combination of field observation records, environmental data, and reported CDV cases were used to generate predictive heat maps and simulate disease spread across susceptible wildlife hosts. Results showed that certain environmental factors and animal density hotspots significantly contribute to higher transmission potential of CDV. Preliminary results suggest that high-risk zones can be identified based on overlapping wildlife movement corridors and human interface areas. This modeling approach offers a valuable tool to guide targeted monitoring, early detection and conservation strategies against CDV outbreaks in wildlife.