On some fundamental challenges in monitoring epidemics

Epidemic models often reflect characteristic features of infectious spreading processes by coupled nonlinear differential equations considering different states of health (such as susceptible, infectious or recovered). This compartmental modelling approach, however, delivers an incomplete picture of the dynamics of epidemics, as it neglects stochastic and network effects, and the role of the measurement process, on which the estimation of epidemiological parameters and incidence values relies. In order to study the related issues, we combine established epidemiological spreading models with a measurement model of the testing process, considering the problems of false positives and false negatives as well as biased sampling. Studying a model-generated ground truth in conjunction with simulated observation processes (virtual measurements) allows one to gain insights into the fundamental limitations of purely data-driven methods when assessing the epidemic situation. We conclude that epidemic monitoring, simulation, and forecasting are wicked problems, as applying a conventional data-driven approach to a complex system with nonlinear dynamics, network effects and uncertainty can be misleading. Nevertheless, some of the errors can be corrected for, using scientific knowledge of the spreading dynamics and the measurement process. We conclude that such corrections should generally be part of epidemic monitoring, modelling and forecasting efforts. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

Into the Facet-Selectivity of Sequenced Amphiphilic Peptoids at the Au-Water Interface

Shape-controlled colloidal nanocrystal syntheses often require aid from facet-selective solution-phase chemical additives to regulate atom addition/migration fluxes or oriented particle attachment. Because of their highly tunable chemical property and robustness to a wide range of experimental conditions, peptoids contribute to a very promising group of next-generation functional chemical additives. To generalize the design philosophy, it is critical to understand the origin of facet selectivity at the molecular level. We employ molecular dynamics simulations and biased sampling methods to investigate the origin of Au(111)-favored adsorption of a peptoid, Nce3Ncp6, that is evidenced to assist the formation of five-fold twinned nanostructures. We find that the facet-selectivity is achieved through a synergistic effect of both molecule-surface and solvent-surface interactions. Extending beyond the single-chain scenario, the order of peptoid-peptoid and peptoid-surface energetics, i.e., peptoid-Au(100) < peptoid-peptoid < peptoid-Au(111), further amplifies the distinct behavior of Nce3Ncp6 chains on different Au surfaces. Our studies set the stage for future peptoid design in shape-controlled nanocrystal syntheses by probing the facet selectivity from various perspectives.

Modeling the effect of age on quantiles of the incubation period distribution of COVID-19

Background The novel coronavirus SARS-CoV-2 (coronavirus disease 2019, COVID-19) has caused serious consequences on many aspects of social life throughout the world since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019. Note that the incubation period distribution is key to the prevention and control efforts of COVID-19. This study aimed to investigate the conditional distribution of the incubation period of COVID-19 given the age of infected cases and estimate its corresponding quantiles from the information of 2172 confirmed cases from 29 provinces outside Hubei in China. Methods We collected data on the infection dates, onset dates, and ages of the confirmed cases through February 16th, 2020. All the data were downloaded from the official websites of the health commission. As the epidemic was still ongoing at the time we collected data, the observations subject to biased sampling. To address this issue, we developed a new maximum likelihood method, which enables us to comprehensively study the effect of age on the incubation period. Results Based on the collected data, we found that the conditional quantiles of the incubation period distribution of COVID-19 vary by age. In detail, the high conditional quantiles of people in the middle age group are shorter than those of others while the low quantiles did not show the same differences. We estimated that the 0.95-th quantile related to people in the age group 23 ∼55 is less than 15 days. Conclusions Observing that the conditional quantiles vary across age, we may take more precise measures for people of different ages. For example, we may consider carrying out an age-dependent quarantine duration in practice, rather than a uniform 14-days quarantine period. Remarkably, we may need to extend the current quarantine duration for people aged 0 ∼22 and over 55 because the related 0.95-th quantiles are much greater than 14 days.

Stochastic modeling uncovers a novel mechanism underlying the evolution of mutation rates in sexually reproducing populations

Natural selection is believed to universally work to lower mutation rates (MR) due to the negative impact of mutations on individual fitness. Mutator alleles have only been found to be co-selected by genetic linkage with adaptive alleles in prokaryotes. Sexual reproduction substantially reduces genetic linkage, allowing selection to effectively eradicate mutator alleles. The current understanding, therefore, is that in sexually reproducing populations selection always works to lower MR, limited by the effective population size that determines the overall selection efficiency. In the present paper, we apply a Monte Carlo model of a sexually reproducing population and demonstrate that selection acting on MR does not universally favor lower MR but depends on the mode of selection acting on adaptive phenotypic traits. We demonstrate a unique previously unreported co-selective process that can drive the evolution of higher MR in sexually reproducing populations. Our results show that MR evolution is significantly influenced by multigenic inheritance of both MR and adaptive traits that are under selection. Our results also show that, contrary to the generally accepted axiom, population size appears not to affect the strength of selection uniformly but likely forms an intra-population gradient that generates a "biased sampling" process that has an opposite effect on selection strength and thus modulates or even negates the effect of population size on MR evolution. Based on our results, we propose an expanded population genetics theory of the evolution of mutation rates in sexually reproducing organisms. Our results have potential implications for understanding processes underlying rapid adaptive change in speciation and related macroevolutionary patterns

Relative species abundance and population densities of the past; developing multi-species occupancy models for fossil data

The number of individuals of species within communities varies, but estimating abundance, given incomplete and biased sampling, is challenging. Here, we describe a new occupancy model in a hierarchical Bayesian framework with random effects, where multi-species occupancy and detection are modeled as a means to estimate relative species abundance and relative population densities. The modelling framework is suited for occupancy data for temporal samples of fossil communities with repeated sampling including multiple species with similar preservation potential. We demonstrate our modelling framework using a fossil community of benthic organisms to estimate changing relative species abundance dynamics and relative population densities of focal species in 9 (geological) time-intervals over 2.3 million years. We also explored potential explanatory factors (paleoenvironmental proxies) and temporal autocorrelation that could provide extra information on unsampled time-intervals. The modelling framework is applicable across a wide range of questions on species-level dynamics in (palaeo)ecological community settings.

How to approach the study of syndromes in macroevolution

Syndromes, wherein multiple traits evolve convergently in response to a shared selective driver, form a central concept in ecology and evolution. Recent work has questioned the utility and indeed the existence of some of the classic syndromes, such as pollination and seed dispersal syndromes. Here, we discuss some of the major issues that have plagued research into syndromes in macroevolution. First, observation of co-evolving traits (sometimes called “trait syndromes'') is often used as evidence of adaptation to a particular driver, even when the link between traits and adaptation is not well-tested. Second, the study of syndromes often uses a biased sampling approach, focusing on the most extreme examples, which may obscure significant continuous variation between traits. Finally, researchers often focus on the traits that are easiest to measure even though these may not be the most directly relevant to adaptive hypotheses. We argue that these issues can be avoided by combining macroevolutionary studies of trait variation across entire clades with explicit tests of adaptive hypotheses, and that taking this approach will lead to a better understanding of syndrome-like evolution and its drivers.