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Measles is an important infectious disease system both for its burden on public health and as an opportunity for studying nonlinear spatio-temporal disease dynamics. Traditional mechanistic models often struggle to fully capture the complex nonlinear spatio-temporal dynamics inherent in measles outbreaks. In this paper, we first develop a high-dimensional feed-forward neural network model with spatial features (SFNN) to forecast endemic measles outbreaks and systematically compare its predictive power with that of a classical mechanistic model (TSIR). We illustrate the utility of our model using England and Wales measles data from 1944-1965. These data present multiple modeling challenges due to the interplay between metapopulations, seasonal trends, and nonlinear dynamics related to demographic changes. Our results show that while the TSIR model yields similarly performant short-term (1 to 2 biweeks ahead) forecasts for highly populous cities, our neural network model (SFNN) consistently achieves lower root mean squared error (RMSE) across other forecasting windows. Furthermore, we show that our spatial-feature neural network model, without imposing mechanistic assumptions a priori, can uncover gravity-model-like spatial hierarchy of measles spread in which major cities play an important role in driving regional outbreaks. We then turn our attention to integrative approaches that combine mechanistic and machine learning models. Specifically, we investigate how the TSIR can be utilized to improve a state-of-the-art approach known as Physics-Informed-Neural-Networks (PINN) which explicitly combines compartmental models and neural networks. Our results show that the TSIR can facilitate the reconstruction of latent susceptible dynamics, thereby enhancing both forecasts in terms of mean absolute error (MAE) and parameter inference of measles dynamics within the PINN. In summary, our results show that appropriately designed neural network-based models can outperform traditional mechanistic models for short to long-term forecasts, while simultaneously providing mechanistic interpretability. Our work also provides valuable insights into more effectively integrating machine learning models with mechanistic models to enhance public health responses to measles and similar infectious disease systems. Author summary: Mechanistic models have been foundational in developing an understanding of the transmission dynamics of infectious diseases including measles. In contrast to their mechanistic counterparts, machine learning techniques including neural networks have primarily focused on improving forecasting accuracy without explicitly inferring transmission dynamics. Effectively integrating these two modeling approaches remains a central challenge. In this paper, we first develop a high-dimensional neural network model to forecast spatiotemporal endemic measles outbreaks and systematically compare its predictive power with that of a classical mechanistic model (TSIR). We illustrate the utility of our model using a detailed dataset describing measles outbreaks in England and Wales from 1944–1965, one of the best-documented and most-studied nonlinear infectious disease systems. Our results show that overall, our neural network model outperforms the TSIR in all forecasting windows. Furthermore, we show that our neural network model can uncover the mechanism of hierarchical spread of measles where major cities drive regional outbreaks. We then develop an integrative approach that explicitly and effectively combines mechanistic and machine learning models, improving simultaneously both forecasting and inference. In summary, our work offers valuable insights into the effective utilization of machine learning models, and integration with mechanistic models, for enhancing outbreak responses to measles and similar infectious disease systems. [ABSTRACT FROM AUTHOR]

Introduction Wastewater-based epidemiology (WBE) has emerged as an effective and sensitive approach for monitoring COVID-19 presence in a community through the detection of the novel coronavirus (SARS-CoV-2) shed by infected [...], Background: Positive correlations have been reported between wastewater SARS-CoV-2 concentrations and a community&apos;s burden of infection, disease or both. However, previous studies mostly compared wastewater to clinical case counts or nonrepresentative convenience samples, limiting their quantitative potential. Objectives: This study examined whether wastewater SARS-CoV-2 concentrations could provide better estimations for SARS-CoV-2 community prevalence than reported cases of COVID-19. In addition, this study tested whether wastewater-based epidemiology methods could identify neighborhood-level COVID-19 hotspots and SARS-CoV-2 variants. Methods: Community SARS-CoV-2 prevalence was estimated from eight randomized door-to-door nasal swab sampling events in six Oregon communities of disparate size, location, and demography over a 10-month period. Simultaneously, wastewater SARS-CoV-2 concentrations were quantified at each community&apos;s wastewater treatment plant and from 22 Newport, Oregon, neighborhoods. SARS-CoV-2 RNA was sequenced from all positive wastewater and nasal swab samples. Clinically reported case counts were obtained from the Oregon Health Authority. Results: Estimated community SARS-CoV-2 prevalence ranged from 8 to 1,687/10,000 persons. Community wastewater SARS- CoV-2 concentrations ranged from 2.9 to 5.1 [log.sub.10] gene copies per liter. Wastewater SARS-CoV-2 concentrations were more highly correlated (Pearson&apos;s r = 0.96; [R.sup.2] = 0.91) with community prevalence than were clinically reported cases of COVID-19 (Pearson&apos;s r = 0.85; [R.sup.2] = 0.73). Monte Carlo simulations indicated that wastewater SARS-CoV-2 concentrations were significantly better than clinically reported cases at estimating prevalence (p < 0.05). In addition, wastewater analyses determined neighborhood-level COVID-19 hot spots and identified SARS-CoV-2 variants (B.1 and B.1.399) at the neighborhood and city scales. Discussion: The greater reliability of wastewater SARS-CoV-2 concentrations over clinically reported case counts was likely due to systematic biases that affect reported case counts, including variations in access to testing and underreporting of asymptomatic cases. With these advantages, combined with scalability and low costs, wastewater-based epidemiology can be a key component in public health surveillance of COVID-19 and other communicable infections. https://doi.org/10.1289/EHP10289

Knowledge of postfire residuals in boreal forest landscapes is increasingly important for ecological applications and forest management. While many studies provide useful insight, knowledge of stand-scale postfire residual occurrence and variability remains fragmented and untested as formal hypotheses. We examined the spatial variability of stand-scale postfire residuals in boreal forests and tested hypotheses of their spatial associations. Based on the literature, we hypothesized that preburn forest cover characteristics, site conditions, proximity to water and fire edge, and local fire intensity influence the spatial variability of postfire residuals. To test these hypotheses, we studied live-tree and snag residuals in 11 boreal Ontario forest fires, using 660 sample points based on high resolution photography (1:408) captured immediately after the fires. The abundance of residuals varied considerably within and among these fires, precluding attempts to generalize estimates. Based on a linear mixed-effects model, our data did not support the hypotheses that preburn forest cover characteristics, site conditions, and proximity to water significantly affect the spatial variability of stand-scale residuals. The results do indicate, however, that stand-scale residual variability is associated with local fire intensity (strongly) and distance to fire edge (weakly). La connaissance des residus apres feu dans les paysages forestiers boreaux prend de plus en plus d'importance pour les applications ecologiques et l'amenagement forestier. Meme si plusieurs etudes fournissent des renseignements utiles, la connaissance de la presence et de la variabilite des residus apres feu a (echelle du peuplement demeure fragmentee et n'a pas ete testees sous forme d'hypotheses formelles. Nous avons etudie la variabilite spatiale des residus apres feu a (echelle du peuplement en foret boreale et nous avons teste des hypotheses reliees a leurs relations spatiales. S'appuyant sur la litterature, nous avons pose l'hypothese que la variabilite spatiale des residus apres feu est influencee par les caracteristiques du couvert anterieur au feu, les conditions de la station, la proximite de Peau et de la limite du feu, et l'intensite locale du feu. Pour tester ces hypotheses, nous avons etudie les arbres vivants et les chicots residuels a la suite de feux survenus dans 11 peuplements boreaux de l'Ontario a l'aide des photographies a haute resolution (1 : 408) de 660 points d'echantillonnage prises immediatement apres le feu. L'abondance des residus variait considerablement a l'interieur et entre les feux, excluant toute tentative de generaliser les estimations. En se basant sur un modele lineaire a effets mixtes, nos donnees n'ont pas confirme les hypotheses selon lesquelles les caracteristiques du couvert anterieur au feu, les conditions de la station et la proximite de l'eau influencent la variabilite spatiale des residus a l'echelle du peuplement. Cependant, les resultats indiquent que la variabilite residuelle a l'echelle du peuplement est associee a l'intensite locale du feu (lien fort) et a la distance de la limite du feu (lien faible). [Traduit par la Redaction], Introduction Forest fires are frequent in boreal North America, burning over 7 x [10.sup.-6] ha annually in Canada alone (Van Wagner 1988), and are considered a major cause of forest [...]

Home range behaviour is a common pattern of space use, having fundamental consequences for ecological processes. However, a general mechanistic explanation is still lacking. Research is split into three separate areas of inquiry – movement models based on random walks, individual-based models based on optimal foraging theory, and a statistical modelling approach – which have developed without much productive contact. Here we review recent advances in modelling home range behaviour, focusing particularly on the problem of identifying mechanisms that lead to the emergence of stable home ranges from unbounded movement paths. We discuss the issue of spatiotemporal scale, which is rarely considered in modelling studies, as well as highlighting the need to consider more closely the dynamical nature of home ranges. Recent methodological and theoretical advances may soon lead to a unified approach, however, conceptually unifying our understanding of linkages among home range behaviour and ecological or evolutionary processes. [ABSTRACT FROM AUTHOR]