Biology subjects

Tildesley, M.

Publications and source records attributed to Tildesley, M..

4 recordsLinked to original sources

bioRxiv2025

Quantifying vector diversion effects in zoonotic systems: A modelling framework for arbovirus transmission between reservoir and dead-end hosts

Vector-borne disease transmission involves complex interactions between vectors, reservoir hosts and dead-end hosts. We present a mathematical model for the vectorial capacity that incorporates multiple host types and their interactions, focusing specifically on West Nile virus transmission by Culex pipiens mosquitoes. Our model integrates climate-dependent parameters affecting vector biology with vector control interventions to predict transmission potential under various scenarios. We demonstrate how vector control interventions targeting one host type can significantly impact transmission dynamics across all host populations. By examining the effects of different vector control tool modes of action (repellency, preprandial killing, disarming and postprandial killing), we develop target product profiles that minimise unintended consequences of vector control. Notably, we identify the optimal intervention characteristics needed to prevent repellency on dead-end hosts from inadvertently increasing transmission among reservoir hosts. This research provides valuable insights for public health officials designing targeted vector control strategies and offers a flexible modelling framework that can be adapted to other vector-borne diseases with complex host dynamics. Author summaryMosquitoes that spread diseases like West Nile virus dont just bite one type of animal--they feed on birds, humans, and other mammals. This creates a complex web of disease transmission that current prevention strategies often overlook. We developed a mathematical model to understand what happens when mosquito control methods target different types of hosts in this network. Our research reveals a surprising and concerning finding: when people use repellents to protect themselves from mosquitoes, those mosquitoes dont simply disappear--they redirect to birds instead. Since birds are the main animals that can spread West Nile virus to other mosquitoes, this redirection can actually increase disease transmission in the bird population by up to 23%. More infected birds ultimately means more infected mosquitoes and higher risk for humans. However, we also identified a solution. We found that if repellent products could kill just 2% of the mosquitoes they encounter before those mosquitoes find alternative hosts, this would eliminate the harmful redirection effect. Our work provides specific guidelines for developing better mosquito control products and helps public health officials understand the broader consequences of different intervention strategies. This framework can be applied to other mosquito-borne diseases beyond West Nile virus.

ecology↗

bioRxiv2024

Allocating Limited Surveillance Effort for Outbreak Detection of Endemic Foot and Mouth Disease

Foot and Mouth Disease (FMD) affects cloven-hoofed animals globally and has become a major economic burden for many countries around the world. Countries that have had recent FMD outbreaks are prohibited from exporting most meat products; this has major economic consequences for farmers in those countries, particularly farmers that experience outbreaks or are near outbreaks. Reducing the number of FMD outbreaks in countries where the disease is endemic is an important challenge that could drastically improve the livelihoods of millions of people. As a result, significant effort is expended on surveillance; but there is a concern that uninformative surveillance strategies may waste resources that could be better used on control management. Rapid detection through sentinel surveillance may be a useful tool to reduce the scale and burden of outbreaks. In this study, we use an extensive outbreak and cattle shipment network dataset from the Republic of Turkiye to test three possible strategies for sentinel surveillance allocation that differ in their data requirements: ranging from low to high data needs, we allocate limited surveillance to [1] farms that frequently send and receive shipments of animals (Network Connectivity), [2] farms near other farms with past outbreaks (Spatial Proximity) and [3] farms that receive many animals from other farms with past outbreaks (Network Proximity). We determine that all of these surveillance methods find a similar number of outbreaks - 2-4.5 times more outbreaks than were detected by surveying farms at random. On average across surveillance efforts, the Network Proximity and Network Connectivity methods each find a similar number of outbreaks and the Spatial Proximity method always finds the fewest outbreaks. Since the Network Proximity method does not outperform the other methods, these results indicate that incorporating both cattle shipment data and outbreak data provides only marginal benefit over the less data-intensive surveillance allocation methods for this objective. We also find that these methods all find more outbreaks when outbreaks are rare. This is encouraging, as early detection is critical for outbreak management. Overall, since the Spatial Proximity and Network Connectivity methods find a similar proportion of outbreaks, and are less data-intensive than the Network Proximity method, countries with endemic FMD whose resources are constrained could prioritize allocating sentinels based on whichever of those two methods requires less additional data collection.

ecology↗

bioRxiv2020

Prospects for detecting early warning signals in discrete event sequence data: application to epidemiological incidence data.

Early warning signals (EWS) identify systems approaching a critical transition, where the system undergoes a sudden change in state. For example, monitoring changes in variance or autocorrelation offers a computationally inexpensive method which can be used in real-time to assess when an infectious disease transitions to elimination. EWS have a promising potential to not only be used to monitor infectious diseases, but also to inform control policies to aid disease elimination. Previously, potential EWS have been identified for prevalence data, however the prevalence of a disease is often not known directly. In this work we identify EWS for incidence data, the standard data type collected by the Centers for Disease Control and Prevention (CDC) or World Health Organization (WHO). We show, through several examples, that EWS calculated on simulated incidence time series data exhibit vastly different behaviours to those previously studied on prevalence data. In particular, the variance displays a decreasing trend on the approach to disease elimination, contrary to that expected from critical slowing down theory; this could lead to unreliable indicators of elimination when calculated on real-world data. We derive analytical predictions which can be generalised for many epidemiological systems, and we support our theory with simulated studies of disease incidence. Additionally, we explore EWS calculated on the rate of incidence over time, a property which can be extracted directly from incidence data. We find that although incidence might not exhibit typical critical slowing down properties before a critical transition, the rate of incidence does, presenting a promising new data type for the application of statistical indicators. Author summaryThe threat posed by infectious diseases has a huge impact on our global society. It is therefore critical to monitor infectious diseases as new data become available during control campaigns. One obstacle in observing disease emergence or elimination is understanding what influences noise in the data and how this fluctuates when near to zero cases. The standard data type collected is the number of new cases per day/month/year but mathematical modellers often focus on data such as the total number of infectious people, due to its analytical properties. We have developed a methodology to monitor the standard type of data to inform whether a disease is approaching emergence or disease elimination. We have shown computationally how fluctuations change as disease data get closer towards a tipping point and our insights highlight how these observed changes can be strikingly different when calculated on different types of data.

ecology↗

bioRxiv2018

A metapopulation model for the 2018 Ebola outbreak in Equateur province in the Democratic Republic of the Congo

Ebola virus disease (EVD) is a viral haemorrhagic fever with high mortality that has caused a number of severe outbreaks in Central and West Africa. Although the majority previous outbreaks have been relatively small, the result of managing outbreaks places huge strains on already limited resources. Mathematical models matched to early case reporting data can be used to identify outbreaks that are at high risk of spreading. Here we consider the EVD outbreak in Equateur Province in the Democratic Republic of the Congo, which was declared on 8 May 2018. We use a simple stochastic metapopulation model to capture the dynamics in the three affected health zones: Bikoro, Iboko and Wangata. We are able to rapidly simulate a large number of realisations and use approximate Bayesian computation, a likelihood-free method, to determine parameters by matching between reported and simulated cases. This method has a number of advantages over more traditional likelihood-based methods as it is less sensitive to errors in the data and is a natural extension to the prediction framework. Using data from 8 to 25 May 2018 we are able to capture the exponential increases in the number of cases in three locations (Bikoro, Iboko and Wangata), although our estimated basic reproductive ratio is higher than for previous outbreaks. Using additional data until 08 July 2018 we are able to detect a decrease in transmission such that the reproductive ratio falls below one. We also estimate the probability of transmission to Kinshasa. We believe this method of fitting models to data offers a generic approach that can deliver rapid results in real time during a range of future outbreaks.

epidemiology↗