Purposes and duties in scientific modelling

More people than ever are paying attention to philosophical questions about epidemiological models, including their susceptibility to the influence of social and ethical values, sufficiency to inform policy decisions under certain conditions, and even their fundamental nature. One important question pertains to the purposes of epidemiological models, for example, are COVID-19 models for ‘prediction’ or ‘projection’? Are they adequate for making causal inferences? Is one of their goals, or virtues, to change individual responses to the pandemic? In this essay, we offer our perspective on these questions and place them in the context of other recent philosophical arguments about epidemiological models. We argue that clarifying the intended purpose of a model, and assessing its adequacy for that purpose, are moral-epistemic duties, responsibilities which pertain to knowledge but have moral significance nonetheless. This moral significance, we argue, stems from the inherent value-ladenness of models, along with the potential for models to be used in political decision making in ways that conflict with liberal values and which could lead to downstream harms. Increasing conversation about the moral significance of modelling, we argue, could help us to resist further eroding our standards of democratic scrutiny in the COVID-19 era.

Epidemiological models based on meteorological variables to forewarn Alternaria blight of rapeseed-mustard

Alternaria blight [Alternaria brassicae (Berk.) Sacc.] is one of the most widespread and harmful maladies of rapeseed-mustard, causing yield loss up to 47 per cent. Meteorological parameters especially temperature, relative humidity and bright sunshine hours play major role in the development of Alternaria blight disease. Infection by the pathogen is highly influenced by meteorological conditions. A well-tested model based on meteorological variables is an efficient tool for forewarning this disease. Epidemiology of Alternaria blight of brassicas was investigated based on long term data during 2003-2018 crop seasons on the disease severity and meteorological variables, which was validated with data for two subsequent years. During this study, meteorological variable-based regression model of forewarning was developed for maximum severity (%) of Alternaria blight on leaves and pods for three locations viz., New Delhi, Hisar (Haryana) and Mohanpur (West Bengal)] in India. Validation of the forewarning models for maximum severity (%) of Alternaria blight proved the efficiency of the targeted forecasts.

Genomic epidemiological models describe pathogen evolution across fitness valleys

Genomics is fundamentally changing epidemiological research. However, exploring hypotheses about pathogen evolution in different epidemiological contexts poses new challenges. Models intertwining pathogen epidemiology and genomic evolution can help understand processes such as the emergence of novel pathogen genotypes with higher transmission or resistance to treatment. In this work, we present Opqua, a computational framework for flexible simulation of pathogen epidemiology and evolution. We use Opqua to study determinants of evolution across fitness valleys. We confirm that competition can limit evolution in high transmission environments and find that low transmission, host mobility, and complex pathogen life cycles facilitate reaching new adaptive peaks through population bottlenecks and decoupling selective pressures. The results show the potential of genomic epidemiological modeling as a tool in infectious disease research.

Transmission of the Invasive Pathogen Phytophthora ramorum from Symptomatic to Healthy Host Plants During a Five-Year Period in California

Reliable data on the transmission of airborne plant pathogens are crucial for the development of epidemiological models and implementation of management strategies. The short-distance vertical transmission of the forest pathogen Phytophthora ramorum from a symptomatic California bay laurel (Umbellularia californica) to healthy containerized rhododendrons (Rhododendron caucasicum × R. ponticum var. album) was monitored for five winters (2016/17 to 2020/21) in a field experiment in Northern California. Transmission events were observed during four winters at a frequency of 1 to 17 per season, but not during the extremely dry winter of 2020/21, and were positively correlated to total rainfall rates. The first leaf symptoms were detected around mid-December and reached the highest numbers in January of most years. Only limited symptom development was observed in the spring, with the last detections in May. The exposure time (the time between the first rainfall after placing a bait plant under the bay laurel and development of symptoms) varied between 3 and over 150 days, with an average between 14 and 21 days. P. ramorum was detected from water samples collected from the canopy of the symptomatic California bay laurel. No horizontal pathogen spread was detected from symptomatic to healthy rhododendrons placed at a distance of 2 to 6 m.

Life-shortening effects of radiological weapons in military operations

Military operations can present risks whose origins may be unconventional. As an example, we can mention those within the spectrum of chemical, biological, radiological, and nuclear (CBRN) defense. This study evaluates, through a computer simulation, an operation in which soldiers face radiological contamination after the triggering of a radiological dispersion device (RDD) in an inhabited urban area. The simulation of the Gaussian scattering (analytical) of the Cs-137 radionuclide is performed using the HotSpot Health Physics codes software. The results of the simulation are evaluated according to two radiological risk domains, referring to high (above 100 mSv) and low integrated radiation doses over 4 continuous days of operation. The radiological risk for developing solid cancer according to specific epidemiological models was estimated. This information served as a basis for estimating the future detriment, that is, the loss of life expectancy (LLE). In addition, the methodology may serve as an instructional resource for tabletop exercises contributing to develop leadership and preparation for decision-making in asymmetric environments.

The effectiveness of Non-pharmaceutical interventions in reducing the COVID-19 contagion in the UK, an observational and modelling study

Epidemiological models used to inform government policies aimed to reduce the contagion of COVID-19, assume that the reproduction number is reduced through Non-Pharmaceutical Interventions (NPIs) leading to physical distancing. Available data in the UK show an increase in physical distancing before the NPIs were implemented and a fall soon after implementation. We aimed to estimate the effect of people’s behaviour on the epidemic curve and the effect of NPIs taking into account this behavioural component. We have estimated the effects of confirmed daily cases on physical distancing and we used this insight to design a behavioural SEIR model (BeSEIR), simulated different scenaria regarding NPIs and compared the results to the standard SEIR. Taking into account behavioural insights improves the description of the contagion dynamics of the epidemic significantly. The BeSEIR predictions regarding the number of infections without NPIs were several orders of magnitude less than the SEIR. However, the BeSEIR prediction showed that early measures would still have an important influence in the reduction of infections. The BeSEIR model shows that even with no intervention the percentage of the cumulative infections within a year will not be enough for the epidemic to resolve due to a herd immunity effect. On the other hand, a standard SEIR model significantly overestimates the effectiveness of measures. Without taking into account the behavioural component, the epidemic is predicted to be resolved much sooner than when taking it into account and the effectiveness of measures are significantly overestimated.

An eco-epidemiological model with general functional response of predator to prey

We consider a nonautonomous eco-epidemiological model with general functions for predation on infected and uninfected preys as well as general functions associated to the vital dynamics of the susceptible prey and predator populations. We obtain persistence and extinction results for the infected prey based on assumptions on auxiliary systems constructed from the disease-free system. We moreover consider an iterative process that can improve the extinction results. We apply our results to general eco-epidemiological models that include several examples existent in the literature.

Stochastic social behavior coupled to COVID-19 dynamics leads to waves, plateaus and an endemic state

It is well recognized that population heterogeneity plays an important role in the spread of epidemics. While individual variations in social activity are often assumed to be persistent, i.e. constant in time, here we discuss the consequences of dynamic heterogeneity. By integrating the stochastic dynamics of social activity into traditional epidemiological models we demonstrate the emergence of a new long timescale governing the epidemic, in broad agreement with empirical data. Our Stochastic Social Activity model captures multiple features of real-life epidemics such as COVID-19, including prolonged plateaus and multiple waves, which are transiently suppressed due to the dynamic nature of social activity. The existence of a long timescale due to the interplay between epidemic and social dynamics provides a unifying picture of how a fast-paced epidemic typically will transition to an endemic state.