Evaluating undercounts in epidemics: response to Maruotti et al. 2022

Maruotti et al. 2022 used a mark-recapture approach to estimate bounds on the true number of monkeypox infections in various countries. These approaches are fundamentally flawed; it is impossible to estimate undercounting based solely on a single stream of reported cases. Simulations based on a Richards curve for cumulative incidence show that, for reasonable epidemic parameters, the proposed methods estimate bounds on the ascertainment ratio of $\approx 0.2-0.5$ roughly independently of the true ascertainment ratio. These methods should not be used

Modelling long-distance seed dispersal in heterogeneous landscapes.

1. Long-distance seed dispersal is difficult to measure, yet key to understanding plant population dynamics and community composition. 2. We used a spatially explicit model to predict the distribution of seeds dispersed long distances by birds into habitat patches of different shapes. All patches were the same type of habitat and size, but varied in shape. They occurred in eight experimental landscapes, each with five patches of four different shapes, 150 m apart in a matrix of mature forest. The model was parameterized with smallscale movement data collected from field observations of birds. In a previous study we validated the model by testing its predictions against observed patterns of seed dispersal in real landscapes with the same types and spatial configuration of patches as in the model. 3. Here we apply the model more broadly, examining how patch shape influences the probability of seed deposition by birds into patches, how dispersal kernels (distributions of dispersal distances) vary with patch shape and starting location, and how movement of seeds between patches is affected by patch shape. 4. The model predicts that patches with corridors or other narrow extensions receive higher numbers of seeds than patches without corridors or extensions. This pattern is explained by edgefollowing behaviour of birds. Dispersal distances are generally shorter in heterogeneous landscapes (containing patchy habitat) than in homogeneous landscapes, suggesting that patches divert the movement of seed dispersers, ‘holding’ them long enough to increase the probability of seed defecation in the patches. Dispersal kernels for seeds in homogeneous landscapes were smooth, whereas those in heterogenous landscapes were irregular. In both cases, long-distance (> 150 m) dispersal was surprisingly common, usually comprising approximately 50% of all dispersal events. 5. Synthesis . Landscape heterogeneity has a large influence on patterns of long-distance seed dispersal. Our results suggest that long-distance dispersal events can be predicted using spatially explicit modelling to scale-up local movements, placing them in a landscape context. Similar techniques are commonly used by landscape ecologists to model other types of movement; they offer much promise to the study of seed dispersal

Toward a comprehensive system for constructing compartmental epidemic models

Compartmental models are valuable tools for investigating infectious diseases. Researchers building such models typically begin with a simple structure where compartments correspond to individuals with different epidemiological statuses, e.g., the classic SIR model which splits the population into susceptible, infected, and recovered compartments. However, as more information about a specific pathogen is discovered, or as a means to investigate the effects of heterogeneities, it becomes useful to stratify models further -- for example by age, geographic location, or pathogen strain. The operation of constructing stratified compartmental models from a pair of simpler models resembles the Cartesian product used in graph theory, but several key differences complicate matters. In this article we give explicit mathematical definitions for several so-called ``model products'' and provide examples where each is suitable. We also provide examples of model stratification where no existing model product will generate the desired result

Context-dependent conservation responses to emerging wildlife diseases

Emerging infectious diseases pose an important threat to wildlife. While established protocols exist for combating outbreaks of human and agricultural pathogens, appropriate management actions before, during, and after the invasion of wildlife pathogens have not been developed. We describe stage-specific goals and management actions that minimize disease impacts on wildlife, and the research required to implement them. Before pathogen arrival, reducing the probability of introduction through quarantine and trade restrictions is key because prevention is more cost effective than subsequent responses. On the invasion front, the main goals are limiting pathogen spread and preventing establishment. In locations experiencing an epidemic, management should focus on reducing transmission and disease, and promoting the development of resistance or tolerance. Finally, if pathogen and host populations reach a stable stage, then recovery of host populations in the face of new threats is paramount. Successful management of wildlife disease requires risk-taking, rapid implementation, and an adaptive approach."Funding was provided by the US National Science Foundation (grants EF-0914866, DGE-0741448, DEB-1115069, DEB-1336290) and the National Institutes of Health (grant 1R010AI090159)."https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/14024

Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission

<p>Abstract</p> <p>Background</p> <p>Computational biology is often associated with genetic or genomic studies only. However, thanks to the increase of computational resources, computational models are appreciated as useful tools in many other scientific fields. Such modeling systems are particularly relevant for the study of complex systems, like the epidemiology of emerging infectious diseases. So far, mathematical models remain the main tool for the epidemiological and ecological analysis of infectious diseases, with SIR models could be seen as an implicit standard in epidemiology. Unfortunately, these models are based on differential equations and, therefore, can become very rapidly unmanageable due to the too many parameters which need to be taken into consideration. For instance, in the case of zoonotic and vector-borne diseases in wildlife many different potential host species could be involved in the life-cycle of disease transmission, and SIR models might not be the most suitable tool to truly capture the overall disease circulation within that environment. This limitation underlines the necessity to develop a standard spatial model that can cope with the transmission of disease in realistic ecosystems.</p> <p>Results</p> <p>Computational biology may prove to be flexible enough to take into account the natural complexity observed in both natural and man-made ecosystems. In this paper, we propose a new computational model to study the transmission of infectious diseases in a spatially explicit context. We developed a multi-agent system model for vector-borne disease transmission in a realistic spatial environment.</p> <p>Conclusion</p> <p>Here we describe in detail the general behavior of this model that we hope will become a standard reference for the study of vector-borne disease transmission in wildlife. To conclude, we show how this simple model could be easily adapted and modified to be used as a common framework for further research developments in this field.</p

Effects of Sample Size on Estimates of Population Growth Rates Calculated with Matrix Models

BACKGROUND: Matrix models are widely used to study the dynamics and demography of populations. An important but overlooked issue is how the number of individuals sampled influences estimates of the population growth rate (lambda) calculated with matrix models. Even unbiased estimates of vital rates do not ensure unbiased estimates of lambda-Jensen's Inequality implies that even when the estimates of the vital rates are accurate, small sample sizes lead to biased estimates of lambda due to increased sampling variance. We investigated if sampling variability and the distribution of sampling effort among size classes lead to biases in estimates of lambda. METHODOLOGY/PRINCIPAL FINDINGS: Using data from a long-term field study of plant demography, we simulated the effects of sampling variance by drawing vital rates and calculating lambda for increasingly larger populations drawn from a total population of 3842 plants. We then compared these estimates of lambda with those based on the entire population and calculated the resulting bias. Finally, we conducted a review of the literature to determine the sample sizes typically used when parameterizing matrix models used to study plant demography. CONCLUSIONS/SIGNIFICANCE: We found significant bias at small sample sizes when survival was low (survival = 0.5), and that sampling with a more-realistic inverse J-shaped population structure exacerbated this bias. However our simulations also demonstrate that these biases rapidly become negligible with increasing sample sizes or as survival increases. For many of the sample sizes used in demographic studies, matrix models are probably robust to the biases resulting from sampling variance of vital rates. However, this conclusion may depend on the structure of populations or the distribution of sampling effort in ways that are unexplored. We suggest more intensive sampling of populations when individual survival is low and greater sampling of stages with high elasticities