pwc-archive/papers-with-abstracts huggingface dataset:
Assessing the effect of sample bias correction in species distribution models
Authors: Nicolas Dubos, Clémentine Préau, Maxime Lenormand, Guillaume Papuga, Sophie Montsarrat, Pierre Denelle, Marine Le Louarn, Stien Heremans, May Roel, Philip Roche, Sandra Luque
Date: 2021-03-12 00:00:00

Open-source biodiversity databases contain a large amount of species occurrence records, but these are often spatially biased, which affects the reliability of species distribution models based on these records. Sample bias correction techniques include data filtering at the cost of record numbers or require considerable additional sampling effort. However, independent data are rarely available and assessment of the correction technique must rely on performance metrics computed with subsets of the only available (biased) data, which may be misleading. Here we assess the extent to which an acknowledged sample bias correction technique is likely to improve models' ability to predict species distributions in the absence of independent data. We assessed the variation in model predictions induced by the correction and model stochasticity. We present an index of the effect of correction relative to model stochasticity, the Relative Overlap Index (ROI). We tested whether the ROI better represented the effect of correction than classic performance metrics and absolute overlap metrics using 64 vertebrate species and 21 virtual species with a generated sample bias. When based on absolute overlaps and cross-validation performance metrics, we found no effect of correction, except for cAUC. When considering its effect relative to model stochasticity, the effect of correction depended on the site and the species. Virtual species enabled us to verify that the correction actually improved distribution predictions and the biological relevance of the selected variables at the sites with a clear gradient of sample bias, and when species distribution predictors are not correlated with sample bias patterns.
Null expectations and null hypothesis testing for the species abundance distribution
Authors: Gabriel Arellano
Date: 2021-03-04 00:00:00

The number of elements (N) and types (S) sampled from an ecological system are among the most powerful constraints on observations of abundance, distribution, and diversity. Together, N and S determine sets of possible forms (i.e., feasible sets) for the species abundance distribution (SAD). There are three approaches to the description of the null SAD (= the average feasible SAD). The first approach is based on the random uniform sampling of surjections. I calculate the probability of a given SAD, given N and S, under this approach (Eq. 4). The second approach is based on the random sampling of compositions. I calculate the probability of a given SAD, given N and S, under this approach (Eq. 8). The third approach is based on the random uniform sampling of partitions. I review the approach, which was developed by Locey & White (2013), and provide some asymptotic results useful for ecologists. The center of a feasible set is a null expectation, which should deviate enough from the alternative models before invoking for biological or ecological mechanisms underlying the SAD. Here, I integrate the feasible set approach with the typical framework of inference in ecology (goodness-of-fit, null hypothesis testing, model comparison, null modelling). I describe how to perform numerical simulations to describe expectations under different approaches to the feasible set. I develop objective or fitness functions to allow the estimation of the most likely SAD using numerical optimization. I provide tools to compare null expectations based on the feasible set approach with the observations, in the context of model comparison and null hypothesis testing.
Effect of Vaccination to COVID-19 Disease Progression
Authors: Randy L. Caga-anan, Michelle N. Raza, Grace Shelda G. Labrador, Ephrime B. Metillo
Date: 2021-03-04 00:00:00

A mathematical model of COVID-19 with minimal compartments is developed. The model is simple enough to fit data on confirmed cases, estimate the hidden infection figure and incorporate the effect of vaccination. With the effect of the new variant being considered, the model fits well with Philippine data of confirmed cases. With the fitted parameters, results show that a second peak, strikingly similar in magnitude to the first, is to be expected if the current transmission rate is increased by about 5\% and already a complete disaster if it will go up to 20\%. The model is then used to estimate time to achieve herd immunity considering natural immunity from being infected and vaccination plan of the country. Results show that if the plan of 200,000 vaccinated people per day is achieved then herd immunity can be attained by March 2022. But if only half of that is achieved the time could go up to February 2023.
Analytical estimation of maximum fraction of infected individuals with one-shot non-pharmaceutical intervention in a hybrid epidemic model
Authors: Naoya Fujiwara, Tomokatsu Onaga, Takayuki Wada, Shouhei Takeuchi, Junji Seto, Tomoki Nakaya, Kazuyuki Aihara
Date: 2021-03-03 00:00:00

Facing a global epidemic of new infectious diseases such as COVID-19, non-pharmaceutical interventions (NPIs), which reduce transmission rates without medical actions, are being implemented around the world to mitigate spreads. One of the problems in assessing the effects of NPIs is that different NPIs have been implemented at different times based on the situation of each country; therefore, few assumptions can be shared about how the introduction of policies affects the patient population. Mathematical models can contribute to further understanding these phenomena by obtaining analytical solutions as well as numerical simulations. In this study, an NPI was introduced into the SIR model for a conceptual study of infectious diseases under the condition that the transmission rate was reduced to a fixed value only once within a finite time duration, and its effect was analyzed numerically and theoretically. It was analytically shown that the maximum fraction of infected individuals and the final size could be larger if the intervention starts too early. The analytical results also suggested that more individuals may be infected at the peak of the second wave with a stronger intervention. This study provides quantitative relationship between the strength of a one-shot intervention and the reduction in the number of patients with no approximation. This suggests the importance of the strength and time of NPIs, although detailed studies are necessary for the implementation of NPIs in complicated real-world environments as the model used in this study is based on various simplifications.
An informed thought experiment exploring the potential for a paradigm shift in aquatic food production
Authors: Caitlin D. Kuempel, Halley E. Froehlich, Benjamin S. Halpern
Date: 2021-03-01 00:00:00
Tasks: Management, Sand

The Neolithic Revolution began c. 10000 years ago and is characterised by the ultimate, near complete transition from hunting and gathering to agricultural food production on land. The Neolithic Revolution is thought to have been catalysed by a combination of local population pressure, cultural diffusion, property rights and climate change. We undertake a thought experiment that examines trends in these key hypothesised catalysts and patters of today to explore whether society could be on a path towards another paradigm shift in food production: away from hunting of wild fish towards a transition to mostly fish farming. We find similar environmental and cultural pressures have driven the rapid rise of aquaculture, during a period that has now been coined the Blue Revolution, providing impetus for such a transition in coming decades to centuries. We also highlight the interacting and often mutually reinforcing impacts of 1)technological and scientific advancement, 2)environmental awareness and collective action and 3)globalisation and trade influencing the trajectory and momentum of the Blue Revolution. We present two qualitative narratives that broadly fall within two future trajectories: 1)a ubiquitous aquaculture transition and 20commercial aquaculture and fisheries coexistence. This scenarios approach aims to encourage logical, forward thinking, and innovative solutions to complex systems dynamics. Scenario-based thought experiments are useful to explore large scale questions, increase the accessibility to a wider readership and ideally catalyse discussion around proactive governance mechanisms. We argue the future is not fixed and society now has greater foresight and capacity to choose the workable balance between fisheries sand aquaculture that supports economic, environmental, cultural and social objectives through combined planning, policies and management.
Expectation of the Site Frequency Spectrum
Authors: Alan R. Rogers, Stephen P. Wooding
Date: 2021-02-27 00:00:00

The site frequency spectrum describes variation among a set of n DNA sequences. Its i'th entry (i=1,2,...,n-1) is the number of nucleotide sites at which the mutant allele is present in i copies. Under selective neutrality, random mating, and constant population size, the expected value of the spectrum is well known but somewhat puzzling. Each additional sequence added to a sample adds an entry to the end of the expected spectrum but does not affect existing entries. This note reviews the reasons for this behavior.
Data-Driven Methods for Present and Future Pandemics: Monitoring, Modelling and Managing
Authors: Teodoro Alamo, Daniel G. Reina, Pablo Millán Gata, Victor M. Preciado, Giulia Giordano
Date: 2021-02-25 00:00:00
Tasks: Epidemiology

This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.
Bioenergetics modelling to analyse and predict the joint effects of multiple stressors: Meta-analysis and model corroboration
Authors: Benoit Goussen, Cecilie Rendal, David Sheffield, Emma Butler, Oliver R. Price, Roman Ashauer
Date: 2021-02-25 00:00:00
Tasks: Decision Making, Management

Understanding the consequences of the combined effects of multiple stressors-including stress from man-made chemicals is important for conservation management, the ecological risk assessment of chemicals, and many other ecological applications. Our current ability to predict and analyse the joint effects of multiple stressors is insufficient to make the prospective risk assessment of chemicals more ecologically relevant because we lack a full understanding of how organisms respond to stress factors alone and in combination. Here, we describe a Dynamic Energy Budget (DEB) based bioenergetics model that predicts the potential effects of single or multiple natural and chemical stressors on life history traits. We demonstrate the plausibility of the model using a meta-analysis of 128 existing studies on freshwater invertebrates. We then validate our model by comparing its predictions for a combination of three stressors (i.e. chemical, temperature, and food availability) with new, independent experimental data on life history traits in the daphnid Ceriodaphnia dubia. We found that the model predictions are in agreement with observed growth curves and reproductive traits. To the best of our knowledge, this is the first time that the combined effects of three stress factors on life history traits observed in laboratory studies have been predicted successfully in invertebrates. We suggest that a re-analysis of existing studies on multiple stressors within the modelling framework outlined here will provide a robust null model for identifying stressor interactions, and expect that a better understanding of the underlying mechanisms will arise from these new analyses. Bioenergetics modelling could be applied more broadly to support environmental management decision making.
Exact site frequency spectra of neutrally evolving tumors: a transition between power laws reveals a signature of cell viability
Authors: Einar Bjarki Gunnarsson, Kevin Leder, Jasmine Foo
Date: 2021-02-23 00:00:00

The site frequency spectrum (SFS) is a popular summary statistic of genomic data. While the SFS of a constant-sized population undergoing neutral mutations has been extensively studied in population genetics, the rapidly growing amount of cancer genomic data has attracted interest in the spectrum of an exponentially growing population. Recent theoretical results have generally dealt with special or limiting cases, such as considering only cells with an infinite line of descent, assuming deterministic tumor growth, or taking large-time or large-population limits. In this work, we derive exact expressions for the expected SFS of a cell population that evolves according to a stochastic branching process, first for cells with an infinite line of descent and then for the total population, evaluated either at a fixed time (fixed-time spectrum) or at the stochastic time at which the population reaches a certain size (fixed-size spectrum). We find that while the rate of mutation scales the SFS of the total population linearly, the rates of cell birth and cell death change the shape of the spectrum at the small-frequency end, inducing a transition between a $1/j^2$ power-law spectrum and a $1/j$ spectrum as cell viability decreases. We show that this insight can in principle be used to estimate the ratio between the rate of cell death and cell birth, as well as the mutation rate, using the site frequency spectrum alone. Although the discussion is framed in terms of tumor dynamics, our results apply to any exponentially growing population of individuals undergoing neutral mutations.
A two-strain SARS-COV-2 model for Germany -- Evidence from a Linearization
Authors: Thomas Götz, Wolfgang Bock, Robert Rockenfeller, Moritz Schäfer
Date: 2021-02-22 00:00:00

Currently, due to the COVID-19 pandemic the public life in most European countries stopped almost completely due to measures against the spread of the virus. Efforts to limit the number of new infections are threatened by the advent of new variants of the SARS-COV-2 virus, most prominent the B.1.1.7 strain with higher infectivity. In this article we consider a basic two-strain SIR model to explain the spread of those variants in Germany on small time scales. For a linearized version of the model we calculate relevant variables like the time of minimal infections or the dynamics of the share of variants analytically. These analytical approximations and numerical simulations are in a good agreement to data reported by the Robert Koch Institute (RKI) in Germany.
Modified SIR Model Yielding a Logistic Solution
Authors: Paul A. Reiser
Date: 2020-06-02 00:00:00
Tasks: model

The SIR pandemic model suffers from an unrealistic assumption: The rate of removal from the infectious class of individuals is assumed to be proportional to the number of infectious individuals. This means that a change in the rate of infection is simultaneous with an equal change in the rate of removal. A more realistic assumption is that an individual is removed at a certain time interval after having been infected. A simple modified SIR model is proposed which implements this delay, resulting in a single delay differential equation which comprises the model. A solution to this equation which is applicable to a pandemic is of the form A+B L(t) where L(t) is a logistic function, and A and B are constants. While the classical SIR model is often an oversimplification of pandemic behavior, it is instructive in that many of the fundamental dynamics and descriptors of pandemics are clearly and simply defined. The logistic model is generally used descriptively, dealing as it does with only the susceptible and infected classes and the rate of transfer between them. The present model presents a full but modified SIR model with a simpler logistic solution which is more realistic and equally instructive.
Antigenic waves of virus-immune co-evolution
Authors: Jacopo Marchi, Michael Lässig, Aleksandra M. Walczak, Thierry Mora
Date: 2021-02-20 00:00:00

The evolution of many microbes and pathogens, including circulating viruses such as seasonal influenza, is driven by immune pressure from the host population. In turn, the immune systems of infected populations get updated, chasing viruses even further away. Quantitatively understanding how these dynamics result in observed patterns of rapid pathogen and immune adaptation is instrumental to epidemiological and evolutionary forecasting. Here we present a mathematical theory of co-evolution between immune systems and viruses in a finite-dimensional antigenic space, which describes the cross-reactivity of viral strains and immune systems primed by previous infections. We show the emergence of an antigenic wave that is pushed forward and canalized by cross-reactivity. We obtain analytical results for shape, speed, and angular diffusion of the wave. In particular, we show that viral-immune co-evolution generates a new emergent timescale, the persistence time of the wave's direction in antigenic space, which can be much longer than the coalescence time of the viral population. We compare these dynamics to the observed antigenic turnover of influenza strains, and we discuss how the dimensionality of antigenic space impacts on the predictability of the evolutionary dynamics. Our results provide a concrete and tractable framework to describe pathogen-host co-evolution.
Estimating the impact of non-pharmaceutical interventions and vaccination on the progress of the COVID-19 epidemic in Mexico: a mathematical approach
Authors: Hugo Flores-Arguedas, José Ariel Camacho-Gutiérrez, Fernando Saldaña
Date: 2021-02-19 00:00:00
Tasks: Bayesian Inference

Non-pharmaceutical interventions have been critical in the fight against the COVID-19 pandemic. However, these sanitary measures have been partially lifted due to socioeconomic factors causing a worrisome rebound of the epidemic in several countries. In this work, we assess the effectiveness of the mitigation implemented to constrain the spread of SARS-CoV-2 in the Mexican territory during 2020. We also investigate to what extent the initial deployment of the vaccine will help to mitigate the pandemic and reduce the need for social distancing and other mobility restrictions. Our modeling approach is based on a simple mechanistic Kermack-McKendrick-type model. To quantify the effect of NPIs, we perform a monthly Bayesian inference using officially published data. The results suggest that in the absence of the sanitary measures, the cumulative number of infections, hospitalizations, and deaths would have been at least twice the official number. Moreover, for low vaccine coverage levels, relaxing NPIs may dramatically increase the disease burden; therefore, safety measures are of critical importance at the early stages of vaccination. The simulations also suggest that it may be more desirable to employ a vaccine with low efficacy but reach a high coverage than a vaccine with high effectiveness but low coverage levels. This supports the hypothesis that single doses to more individuals will be more effective than two doses for every person.
Hospitalization dynamics during the first COVID-19 pandemic wave: SIR modelling compared to Belgium, France, Italy, Switzerland and New York City data
Authors: Gregory Kozyreff
Date: 2020-07-02 00:00:00

Using the classical Susceptible-Infected-Recovered epidemiological model, an analytical formula is derived for the number of beds occupied by Covid-19 patients. The analytical curve is fitted to data in Belgium, France, New York City and Switzerland, with a correlation coefficient exceeding 98.8%, suggesting that finer models are unnecessary with such macroscopic data. The fitting is used to extract estimates of the doubling time in the ascending phase of the epidemic, the mean recovery time and, for those who require medical intervention, the mean hospitalization time. Large variations can be observed among different outbreaks.
A stochastic SIR model for the analysis of the COVID-19 Italian epidemic
Authors: Sara Pasquali, Antonio Pievatolo, Antonella Bodini, Fabrizio Ruggeri
Date: 2021-02-15 00:00:00

We propose a stochastic SIR model, specified as a system of stochastic differential equations, to analyse the data of the Italian COVID-19 epidemic, taking also into account the under-detection of infected and recovered individuals in the population. We find that a correct assessment of the amount of under-detection is important to obtain reliable estimates of the critical model parameters. Moreover, a single SIR model over the whole epidemic period is unable to correctly describe the behaviour of the pandemic. Then, the adaptation of the model in every time-interval between relevant government decrees that implement contagion mitigation measures, provides short-term predictions and a continuously updated assessment of the basic reproduction number.
Optimal Vaccination and Treatment Strategies in Reduction of COVID-19 Burden
Authors: Bishal Chhetri, D. k. k. Vamsi, S Balasubramanian, Carani B Sanjeevi
Date: 2021-02-19 00:00:00

In this study, we formulate a mathematical model incorporating age specific transmission dynamics of COVID-19 to evaluate the role of vaccination and treatment strategies in reducing the size of COVID-19 burden. Initially, we establish the positivity and boundedness of the solutions of the model and calculate the basic reproduction number. We then formulate an optimal control problem with vaccination and treatment as control variables. Optimal vaccination and treatment policies are analysed for different values of the weight constant associated with the cost of vaccination and different transmissibility levels. Findings from these suggested that the combined strategies(vaccination and treatment) worked best in minimizing the infection and disease induced mortality. In order to reduce COVID-19 infection and COVID-19 induced deaths to maximum, it was observed that optimal control strategy should be prioritized to population with age greater than 40 years. Not much difference was found between individual strategies and combined strategies in case of mild epidemic ($R_0 \in (0, 2)$). For higher values of $R_0 (R_0 \in (2, 10))$ the combined strategies was found to be best in terms of minimizing the overall infection. The infection curves varying the efficacies of the vaccines were also analysed and it was found that higher efficacy of the vaccine resulted in lesser number of infection and COVID induced death.