Geology topics

Ting Fung Ma

Publications and source records attributed to Ting Fung Ma.

5 recordsLinked to original sources

USGS2025

Learning complex spatial dynamics of wildlife diseases with machine learning-guided partial differential equations

Emerging wildlife pathogens often display geographic variability due to landscape heterogeneity. Modeling approaches capable of learning complex, non-linear spatial dynamics of diseases are needed to rigorously assess and mitigate the effects of pathogens on wildlife health and biodiversity. We propose a novel machine learning (ML)-guided approach that leverages prior physical knowledge of ecological systems, using partial differential equations. We present our approach, taking advantage of the universal function approximation property of neural networks for flexible representation of the underlying dynamics of the geographic spread and growth of wildlife diseases. We demonstrate the benefits of our approach by comparing its forecasting power with commonly used methods and highlighting the obtained insights on disease dynamics. Additionally, we show the theoretical guarantees for the approximation error of our model. We illustrate the implementation of our ML-guided approach using data from white-nose syndrome (WNS) outbreaks in bat populations across the US. WNS is an infectious fungal disease responsible for significant declines in bat populations. Our results on WNS are useful for disease surveillance and bat conservation efforts. Our methods can be broadly used to assess the effects of environmental and anthropogenic drivers impacting wildlife health and biodiversity.

Environmental Data Science↗

USGS2025

Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids

Spatiotemporal causal inference methods are needed to detect the effect of interventions on indirectly measured epidemiological outcomes that go beyond studying spatiotemporal correlations. Chronic wasting disease (CWD) causes neurological degeneration and eventual death to white-tailed deer ( Odocoileus virginianus ) in Wisconsin. Targeted culling involves removing deer after traditional hunting seasons in areas with high CWD prevalence. The evaluation of the causal effects of targeted culling in the spread and growth of CWD is an important unresolved research and CWD management question that can guide surveillance efforts. Reaction–diffusion partial differential equations (PDEs) can be used to mechanistically model the underlying spatiotemporal dynamics of wildlife diseases, like CWD, allowing researchers to make inference about unobserved epidemiological quantities. These models indirectly regress spatiotemporal covariates on diffusion and growth rates parameterizing such PDEs, obtaining associational conclusions. In this work we develop an innovative method to obtain causal estimators for the effect of targeted culling interventions on CWD epidemiological processes using an inverse-probability-of-treatment-weighted technique by means of marginal structural models embedded in the PDE fitting process. Additionally we establish a novel scheme for sensitivity analysis under unmeasured confounder for testing the hypothesis of a significant causal effect in the indirectly measured epidemiological outcomes. Our methods can be broadly used to study the impact of spatiotemporal interventions and treatment exposures in the epidemiological evolution of infectious diseases that can help to inform future efforts to mitigate public health implications and wildlife disease burden.

Wisconsin↗

USGS2024

Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease

To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINN) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatiotemporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.

Spatial Statistics↗

USGS2023

Model-based surveillance system design under practical constraints with application to white-nose syndrome

Infectious diseases are powerful ecological forces structuring ecosystems, causing devastating economic impacts and disrupting society. Successful prevention and control of pathogens requires knowledge of the current scope and severity of disease, as well as the ability to forecast future disease dynamics. Assessment of the current situation as well as prediction of the future conditions, rely on spatially referenced information regarding the presence or absence of a pathogen, and the prevalence of the pathogen in the population. In particular, knowledge about the location of the disease front is foundational for deploying disease countermeasures to prevent further disease spread and focusing control efforts to reduce disease intensity in affected areas. In this paper, we develop a model-based approach to designing sampling strategies for wildlife disease surveillance at the disease front. Specifically, we use a mechanistic spatio-temporal model based on an underlying partial differential equation to track the disease dynamics and predict the disease prevalence in the future. We also devise an optimal surveillance system design at the disease front that takes into account the practical constraints of sampling. We evaluate the effectiveness of our proposed design via a simulation study and demonstrate the application of the proposed approach by designing a surveillance strategy for the pathogen that causes white-nose syndrome.

Environmental and Ecological Statistics↗

USGS2019

Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses

Influenza A viruses are one of the most significant viral groups globally with substantial impacts on human, domestic animal and wildlife health. Wild birds are the natural reservoirs for these viruses, and active surveillance within wild bird populations provides critical information about viral evolution forming the basis of risk assessments and countermeasure development. Unfortunately, active surveillance programs are often resource‐intensive, and thus, enhancing programs for increased efficiency is paramount. Machine learning, a branch of artificial intelligence applications, provides statistical learning procedures that can be used to gain novel insights into disease surveillance systems. We use a form of machine learning, gradient boosted trees, to estimate the probability of isolating avian influenza viruses (AIV) from wild bird samples collected during surveillance for AIVs from 2006 to 2011 in the United States. We examined several predictive features including age, sex, bird type, geographic location and matrix gene rRT‐PCR results. Our final model had high predictive power and only included geographic location and rRT‐PCR results as important predictors. The highest predicted viral isolation probability was for samples collected from the north‐central states and the south‐eastern region of Alaska. Lower rRT‐PCR Ct‐values are associated with increased likelihood of AIV isolation, and the model estimated 16% probability of isolating AIV from samples declared negative (i.e., ≥35 Ct‐value) using the rRT‐PCR screening test and standard protocols. Our model can be used to prioritize previously collected samples for isolation and rapidly evaluate AIV surveillance designs to maximize the probability of viral isolation given limited resources and laboratory capacity.

Transboundary and Emerging Diseases↗