Ecological and epidemiological models are both useful for SARS-CoV-2

Climate correlates of bluetongue incidence in southern Portugal

Model forecasts of the spatiotemporal occurrence dynamics of diseases are necessary and can help understand and thus manage future disease outbreaks. In our study, we used ecological niche modelling to assess the impact of climate on the vector suitability for bluetongue disease, a disease affecting livestock production with important economic consequences. Specifically, we investigated the relationship between the occurrence of bluetongue outbreaks and the environmental suitability of each of the four vector species studied. We found that the main vector for bluetongue disease, Culicoides imicola, a typically tropical and subtropical species, was a strong predictor for disease outbreak occurrence in a region of southern Portugal from 2004 to 2021. The results highlight the importance of understanding the climatic factors that might influence vector presence to help manage infectious disease impacts. When diseases impact economically relevant species, the impacts go beyond mortality and have important economic consequences.

"Does a Respiratory Virus Have an Ecological Niche, and If So, Can It Be Mapped?" Yes and Yes

Although the utility of Ecological Niche Models (ENM) and Species Distribution Models (SDM) has been demonstrated in many ecological applications, their suitability for modelling epidemics or pandemics, such as SARS-Cov-2, has been questioned. In this paper, contrary to this viewpoint, we show that ENMs and SDMs can be created that can describe the evolution of pandemics, both in space and time. As an illustrative use case, we create models for predicting confirmed cases of COVID-19, viewed as our target "species", in Mexico through 2020 and 2021, showing that the models are predictive in both space and time. In order to achieve this, we extend a recently developed Bayesian framework for niche modelling, to include: (i) dynamic, non-equilibrium "species" distributions; (ii) a wider set of habitat variables, including behavioural, socio-economic and socio-demographic variables, as well as standard climatic variables; (iii) distinct models and associated niches for different species characteristics, showing how the niche, as deduced through presence-absence data, can differ from that deduced from abundance data. We show that the niche associated with those places with the highest abundance of cases has been highly conserved throughout the pandemic, while the inferred niche associated with presence of cases has been changing. Finally, we show how causal chains can be inferred and confounding identified by showing that behavioural and social factors are much more predictive than climate and that, further, the latter is confounded by the former.

Preventive measures focused on the urban-rural interface protect rural food-producing communities from SARS-CoV-2

INTRODUCTION

Rural food-producing communities are fundamental for the development of economic activities associated with sustainability and food security. However, despite the importance of rurality in Colombia, preventive strategies continue to be implemented homogeneously, without considering the dynamics of SARS-CoV-2 in rural food-producing communities.

OBJECTIVE

To model real areas in Colombia involving rural and urban populations that have intrinsic SARS-CoV-2 transmission dynamics. Characterize rural-urban interactions by means of a parameter that provides different scenarios and allows us to identify interactions capable of preventing SARS-CoV-2 transmission in rural food-producing communities.

MATERIALS AND METHODS

The dynamics of SARS-CoV-2 infection was modeled in five case studies (Boyacá, Caquetá, Cundinamarca, Santander and Sucre) considering urban and rural areas and their interaction (connectivity) in the urban-rural interface. For this purpose, an epidemiological compartmental model considering a classification of individuals according to their economic activity and their epidemiological status was assessed.

RESULTS

Preventive measures focused on the urban-rural interface impact the number of deaths in rural areas. Hence, it is possible to assume that the dynamics of the disease in rural areas depend on the constant interaction with infected individuals from urban areas, which occurs due to the food production dynamics in the urban-rural interface.

CONCLUSIONS

Preventive measures should focus on places of high transmissibility and risk for rural communities, such as the urban-rural interface. This work highlights the importance of national heterogeneous preventive measures and the protection of rural communities from the social and economic impacts of SARS-CoV-2.

Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year

The present work aims to give an overview on the international scientific papers related to the territorial spreading of SARS-CoV-2, with a specific focus upon applied quantitative geography and territorial analysis, to define a general structure for epidemiological geography research. The target publications were based on GIS spatial analysis, both in the sense of topological analysis and descriptive statistics or lato sensu geographical approaches. The first basic purpose was to organize and enhance the vast knowledge developments generated hitherto by the first pandemic that was studied "on-the-fly" all over the world. The consequent target was to investigate to what extent researchers in geography were able to draw scientifically consistent conclusions about the pandemic evolution, as well as whether wider generalizations could be reasonably claimed. This implied an analysis and a comparison of their findings. Finally, we tested what geographic approaches can say about the pandemic and whether a reliable spatial analysis routine for mapping infectious diseases could be extrapolated. We selected papers proposed for publication during 2020 and 209 articles complied with our parameters of query. The articles were divided in seven categories to enhance existing commonalities. In some cases, converging conclusions were extracted, and generalizations were derived. In other cases, contrasting or inconsistent findings were found, and possible explanations were provided. From the results of our survey, we extrapolated a routine for the production of epidemiological geography analyses, we highlighted the different steps of investigation that were attained, and we underlined the most critical nodes of the methodology. Our findings may help to point out what are the most critical conceptual challenges of epidemiological mapping, and where it might improve to engender informed conclusions and aware outcomes.

Visualizing the efficacy of vaccination in different Indian states: a comparative account with other countries

Efficacy of the vaccination program for COVID-19 pandemic caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome-CoronaVirus-2) was analyzed. The data for number of cases and vaccination, obtained from government websites from 1st January, 2020 to 10th October, 2021 for India, USA, United Kingdom, Italy, South Africa and states West Bengal, Delhi, Maharashtra, Kerala and Tamil Nadu. Modified Poisson prediction models were developed using SPSS to predict the number of cases with and without vaccination as predictor to catalogue the efficacy of vaccination. In the prediction model where vaccination was used as a predictor, the downward trend of predicted value matched with actual value, falling within the 95% confidence interval. However, individual peaks of waves were not observed clearly for the predicted values. Vaccination within the population was observed to be very critical in controlling the pandemic progression with all countries showing decrease in daily case counts as more people in the population got vaccinated. In India, owing to the huge population, more vaccination is needed for the predicted cases to fall lower.

Evolution and spread of SARS-CoV-2 likely to be affected by climate

COVID-19 pandemic has been a subject of extensive study. However, it is still unclear why it was restricted to higher latitudes during the initial days and later cascaded in the tropics. Here, we analyzed 176 SARS-CoV-2 genomes across different climate zones and Köppen's climate that provided insights about within-species virus evolution and its relation to abiotic factors. Two genetically variant groups, named G1 and G2, were identified, well defined by four mutations. The G1 group (ancestor) is mainly restricted to warm and moist, temperate climate (Köppen's C climate) while its descendent G2 group surpasses the climatic restrictions of G1, initially cascading into neighboring cold climate (D) of higher latitudes and later into the hot climate of the tropics (A). It appears that the gradation of temperate climate (Cfa-Cfb) to cold climate (Dfa-Dfb) drives the evolution of G1 into the G2 variant group, which later adapted to tropical climate (A) as well. It seems this virus followed an inverse latitudinal gradient in the beginning due to its preference towards temperate (C) and cold climate (D). Our work elucidates virus evolutionary studies combined with climatic studies can provide crucial information about the pathogenesis and natural spreading pathways in such outbreaks, which is hard to achieve through individual studies. Mutational insights gained may help design an efficacious vaccine.

On the usefulness of the bioclimatic correlative models of SARS-CoV-2

This paper addresses the effects of atmospheric conditions on the spread of the SARS-CoV-2 coronavirus and its associated disease, COVID-19. For this purpose, we assess the limitations of bioclimatic correlative models to explain the geographic distribution of SARS-CoV-2 in the context of medical geography. Overall, there is a broad consensus that the global distribution of COVID-19 is not random but conditioned by environmental drivers. However, as the COVID-19 distribution becomes global, including tropical climates, the evidence reveals that atmospheric conditions explain, at most, only a limited amount of the space-time dynamics of SARS-CoV-2. Therefore, the usefulness of approaches based on bioclimatic envelopes is in question since the dominant route for the spread of COVID-19 seems to be the anthroposphere's non-stationary environment. In this sense, there is a need to clarify further the role of different transmission routes at multiple scales and outdoor and indoor environments beyond bioclimatic envelopes. At this time, the possible influence of the weather in COVID-19 spread is not sufficient to be taken into account in public health policies. Hence, until reliable bioclimatic envelopes of SARS-CoV-2, if any, are found, caution should be exercised when reporting, as this could have unforeseen consequences.

Containment measures limit environmental effects on COVID-19 early outbreak dynamics

Environmental factors are well known to affect spatio-temporal patterns of infectious disease outbreaks, but whether the rapid spread of COVID-19 across the globe is related to local environmental conditions is highly debated. We assessed the impact of environmental factors (temperature, humidity and air pollution) on the global patterns of COVID-19 early outbreak dynamics during January-May 2020, controlling for several key socio-economic factors and airport connections. We showed that during the earliest phase of the global outbreak (January-March), COVID-19 growth rates were non-linearly related to climate, with fastest spread in regions with a mean temperature of ca. 5 °C, and in the most polluted regions. However, environmental effects faded almost completely when considering later outbreaks, in keeping with the progressive enforcement of containment actions. Accordingly, COVID-19 growth rates consistently decreased with stringent containment actions during both early and late outbreaks. Our findings indicate that environmental drivers may have played a role in explaining the early variation among regions in disease spread. With limited policy interventions, seasonal patterns of disease spread might emerge, with temperate regions of both hemispheres being most at risk of severe outbreaks during colder months. Nevertheless, containment measures play a much stronger role and overwhelm impacts of environmental variation, highlighting the key role for policy interventions in curbing COVID-19 diffusion within a given region. If the disease will become seasonal in the next years, information on environmental drivers of COVID-19 can be integrated with epidemiological models to inform forecasting of future outbreak risks and improve management plans.

Spatial modeling could not differentiate early SARS-CoV-2 cases from the distribution of humans on the basis of climate in the United States

The SARS-CoV-2 coronavirus is wreaking havoc globally, yet, as a novel pathogen, knowledge of its biology is still emerging. Climate and seasonality influence the distributions of many diseases, and studies suggest at least some link between SARS-CoV-2 and weather. One such study, building species distribution models (SDMs), predicted SARS-CoV-2 risk may remain concentrated in the Northern Hemisphere, shifting northward in summer months. Others have highlighted issues with SARS-CoV-2 SDMs, notably: the primary niche of the virus is the host it infects, climate may be a weak distributional predictor, global prevalence data have issues, and the virus is not in population equilibrium. While these issues should be considered, we believe climate's relationship with SARS-CoV-2 is still worth exploring, as it may have some impact on the distribution of cases. To further examine if there is a link to climate, we build model projections with raw SARS-CoV-2 case data and population-scaled case data in the USA. The case data were from across March 2020, before large travel restrictions and public health policies were impacting cases across the country. We show that SDMs built from population-scaled case data cannot be distinguished from control models (built from raw human population data), while SDMs built on raw case data fail to predict the known distribution of cases in the U.S. from March. The population-scaled analyses indicate that climate did not play a central role in early U.S. viral distribution and that human population density was likely the primary driver. We do find slightly more population-scaled viral cases in cooler areas. Ultimately, the temporal and geographic constraints on this study mean that we cannot rule out climate as a partial driver of the SARS-CoV-2 distribution. Climate's role on SARS-CoV-2 should continue to be cautiously examined, but at this time we should assume that SARS-CoV-2 will continue to spread anywhere in the U.S. where governmental policy does not prevent spread.

Spatial modeling could not differentiate early SARS-CoV-2 cases from the distribution of humans on the basis of climate in the United States

The SARS-CoV-2 coronavirus is wreaking havoc globally, yet, as a novel pathogen, knowledge of its biology is still emerging. Climate and seasonality influence the distributions of many diseases, and studies suggest at least some link between SARS-CoV-2 and weather. One such study, building species distribution models (SDMs), predicted SARS-CoV-2 risk may remain concentrated in the Northern Hemisphere, shifting northward in summer months. Others have highlighted issues with SARS-CoV-2 SDMs, notably: the primary niche of the virus is the host it infects, climate may be a weak distributional predictor, global prevalence data have issues, and the virus is not in population equilibrium. While these issues should be considered, we believe climate's relationship with SARS-CoV-2 is still worth exploring, as it may have some impact on the distribution of cases. To further examine if there is a link to climate, we build model projections with raw SARS-CoV-2 case data and population-scaled case data in the USA. The case data were from across March 2020, before large travel restrictions and public health policies were impacting cases across the country. We show that SDMs built from population-scaled case data cannot be distinguished from control models (built from raw human population data), while SDMs built on raw case data fail to predict the known distribution of cases in the U.S. from March. The population-scaled analyses indicate that climate did not play a central role in early U.S. viral distribution and that human population density was likely the primary driver. We do find slightly more population-scaled viral cases in cooler areas. Ultimately, the temporal and geographic constraints on this study mean that we cannot rule out climate as a partial driver of the SARS-CoV-2 distribution. Climate's role on SARS-CoV-2 should continue to be cautiously examined, but at this time we should assume that SARS-CoV-2 will continue to spread anywhere in the U.S. where governmental policy does not prevent spread.