Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Heterologous cAd3-Ebola and MVA-EbolaZ vaccines are safe and immunogenic in US and Uganda phase 1/1b trials

Ebola virus disease (EVD) is a filoviral infection caused by virus species of the Ebolavirus genus including Zaire ebolavirus (EBOV) and Sudan ebolavirus (SUDV). We investigated the safety and immunogenicity of a heterologous prime-boost regimen involving a chimpanzee adenovirus 3 vectored Ebola vaccine [either monovalent (cAd3-EBOZ) or bivalent (cAd3-EBO)] prime followed by a recombinant modified vaccinia virus Ankara EBOV vaccine (MVA-EbolaZ) boost in two phase 1/1b randomized open-label clinical trials in healthy adults in the United States (US) and Uganda (UG). Trial US (NCT02408913) enrolled 140 participants, including 26 EVD vaccine-naïve and 114 cAd3-Ebola-experienced participants (April-November 2015). Trial UG (NCT02354404) enrolled 90 participants, including 60 EVD vaccine-naïve and 30 DNA Ebola vaccine-experienced participants (February-April 2015). All tested vaccines and regimens were safe and well tolerated with no serious adverse events reported related to study products. Solicited local and systemic reactogenicity was mostly mild to moderate in severity. The heterologous prime-boost regimen was immunogenic, including induction of durable antibody responses which peaked as early as two weeks and persisted up to one year after each vaccination. Different prime-boost intervals impacted the magnitude of humoral and cellular immune responses. The results from these studies demonstrate promising implications for use of these vaccines in both prophylactic and outbreak settings.

The impact of anthropogenic climate change on pediatric viral diseases

The adverse effects of climate change on human health are unfolding in real time. Environmental fragmentation is amplifying spillover of viruses from wildlife to humans. Increasing temperatures are expanding mosquito and tick habitats, introducing vector-borne viruses into immunologically susceptible populations. More frequent flooding is spreading water-borne viral pathogens, while prolonged droughts reduce regional capacity to prevent and respond to disease outbreaks with adequate water, sanitation, and hygiene resources. Worsening air quality and altered transmission seasons due to an increasingly volatile climate may exacerbate the impacts of respiratory viruses. Furthermore, both extreme weather events and long-term climate variation are causing the destruction of health systems and large-scale migrations, reshaping health care delivery in the face of an evolving global burden of viral disease. Because of their immunological immaturity, differences in physiology (e.g., size), dependence on caregivers, and behavioral traits, children are particularly vulnerable to climate change. This investigation into the unique pediatric viral threats posed by an increasingly inhospitable world elucidates potential avenues of targeted programming and uncovers future research questions to effect equitable, actionable change. IMPACT: A review of the effects of climate change on viral threats to pediatric health, including zoonotic, vector-borne, water-borne, and respiratory viruses, as well as distal threats related to climate-induced migration and health systems. A unique focus on viruses offers a more in-depth look at the effect of climate change on vector competence, viral particle survival, co-morbidities, and host behavior. An examination of children as a particularly vulnerable population provokes programming tailored to their unique set of vulnerabilities and encourages reflection on equitable climate adaptation frameworks.

Modeling Supply and Demand Dynamics of Vaccines against Epidemic-Prone Pathogens: Case Study of Ebola Virus Disease

Health emergencies caused by epidemic-prone pathogens (EPPs) have increased exponentially in recent decades. Although vaccines have proven beneficial, they are unavailable for many pathogens. Furthermore, achieving timely and equitable access to vaccines against EPPs is not trivial. It requires decision-makers to capture numerous interrelated factors across temporal and spatial scales, with significant uncertainties, variability, delays, and feedback loops that give rise to dynamic and unexpected behavior. Therefore, despite progress in filling R&D gaps, the path to licensure and the long-term viability of vaccines against EPPs continues to be unclear. This paper presents a quantitative system dynamics modeling framework to evaluate the long-term sustainability of vaccine supply under different vaccination strategies. Data from both literature and 50 expert interviews are used to model the supply and demand of a prototypical Ebolavirus Zaire (EBOV) vaccine. Specifically, the case study evaluates dynamics associated with proactive vaccination ahead of an outbreak of similar magnitude as the 2018-2020 epidemic in North Kivu, Democratic Republic of the Congo. The scenarios presented demonstrate how uncertainties (e.g., duration of vaccine-induced protection) and design criteria (e.g., priority geographies and groups, target coverage, frequency of boosters) lead to important tradeoffs across policy aims, public health outcomes, and feasibility (e.g., technical, operational, financial). With sufficient context and data, the framework provides a foundation to apply the model to a broad range of additional geographies and priority pathogens. Furthermore, the ability to identify leverage points for long-term preparedness offers directions for further research.

An immobilization-free electrochemical biosensor based on CRISPR/Cas13a and FAM-RNA-MB for simultaneous detection of multiple pathogens

To better respond to biosecurity issues, we need to build good technology and material reserves for pathogenic microorganism screening. Here, we designed an electrochemical/optical signal probe with a common fluorophore and an electrochemically active group, breaking the previous perception that the signal probe is composed of a fluorophore and a quenching group and realizing the response of three signals: electrochemistry, fluorescence, and direct observation. Then, we proposed a homogeneous electrochemical nucleic acid detection system based on CRISPR/Cas named "HELEN-CR" by integrating free electrochemical/optical signal probes and Cas13a cleavage, achieving a limit of detection of 1 pM within 25 min. To improve the detection sensitivity, we applied recombinase polymerase amplification to amplify the target nucleic acid, achieving a limit of detection of 30 zM within 45 min. Complemented by our self-developed multi-chamber microfluidic chip and portable electrochemical instrument, simultaneous detection of multiple pathogens can be achieved within 50 min, facilitating minimally trained personnel to obtain detection results quickly in a difficult environment. This study proposes a simple, scalable, and general idea and solution for the rapid detection of pathogenic microorganisms and biosecurity monitoring.

Integrating databases for spatial analysis of parasite-host associations and the novel Brazilian dataset

Incomplete information on parasites, their associated hosts, and their precise geographical location hampers the ability to predict disease emergence in Brazil, a continental-sized country characterised by significant regional disparities. Here, we demonstrate how the NCBI Nucleotide and GBIF databases can be used as complementary databases to study spatially georeferenced parasite-host associations. We also provide a comprehensive dataset of parasites associated with mammal species that occur in Brazil, the Brazilian Mammal Parasite Occurrence Data (BMPO). This dataset integrates wild mammal species' morphological and life-history traits, zoonotic parasite status, and zoonotic microparasite transmission modes. Through meta-networks, comprising interconnected host species linked by shared zoonotic microparasites, we elucidate patterns of zoonotic microparasite dissemination. This approach contributes to wild animal and zoonoses surveillance, identifying and targeting host species accountable for disproportionate levels of parasite sharing within distinct biomes. Moreover, our novel dataset contributes to the refinement of models concerning disease emergence and parasite distribution among host species.

The Effect of Previous Exposure to Malaria Infection and Clinical Malaria Episodes on the Immune Response to the Two-Dose Ad26.ZEBOV, MVA-BN-Filo Ebola Vaccine Regimen

We assessed whether the immunogenicity of the two-dose Ad26.ZEBOV, MVA-BN-Filo Ebola vaccine regimen with a 56-day interval between doses was affected by exposure to malaria before dose 1 vaccination and by clinical episodes of malaria in the period immediately after dose 1 and after dose 2 vaccinations. Previous malaria exposure in participants in an Ebola vaccine trial in Sierra Leone (ClinicalTrials.gov: NCT02509494) was classified as low, intermediate, and high according to their antibody responses to a panel of Plasmodium falciparum antigens detected using a Luminex MAGPIX platform. Clinical malaria episodes after vaccinations were recorded as part of the trial safety monitoring. Binding antibody responses against the Ebola virus (EBOV) glycoprotein (GP) were measured 57 days post dose 1 and 21 days post dose 2 by ELISA and summarized as Geometric Mean Concentrations (GMCs). Geometric Mean Ratios (GMRs) were used to compare groups with different levels of exposure to malaria. Overall, 587 participants, comprising 188 (32%) adults (aged ≥ 18 years) and 399 (68%) children (aged 1-3, 4-11, and 12-17 years), were included in the analysis. There was no evidence that the anti-EBOV-GP antibody GMCs post dose 1 and post dose 2 differed between categories of previous malaria exposure. There was weak evidence that the GMC at 57 days post dose 1 was lower in participants who had had at least one episode of clinical malaria post dose 1 compared to participants with no diagnosed clinical malaria in the same period (GMR = 0.82, 95% CI: 0.69-0.98, p-value = 0.02). However, GMC post dose 2 was not reduced in participants who experienced clinical malaria post-dose 1 and/or post-dose 2 vaccinations. In conclusion, the Ad26.ZEBOV, MVA-BN-Filo Ebola vaccine regimen is immunogenic in individuals with previous exposure to malaria and in those who experience clinical malaria after vaccination. This vaccine regimen is suitable for prophylaxis against Ebola virus disease in malaria-endemic regions.

Using mechanistic models to highlight research priorities for tick-borne zoonotic diseases: Improving our understanding of the ecology and maintenance of Kyasanur Forest Disease in India

The risk of spillover of zoonotic diseases to humans is changing in response to multiple environmental and societal drivers, particularly in tropical regions where the burden of neglected zoonotic diseases is highest and land use change and forest conversion is occurring most rapidly. Neglected zoonotic diseases can have significant impacts on poor and marginalised populations in low-resource settings but ultimately receive less attention and funding for research and interventions. As such, effective control measures and interventions are often hindered by a limited ecological evidence base, which results in a limited understanding of epidemiologically relevant hosts or vectors and the processes that contribute to the maintenance of pathogens and spillover to humans. Here, we develop a generalisable next generation matrix modelling framework to better understand the transmission processes and hosts that have the greatest contribution to the maintenance of tick-borne diseases with the aim of improving the ecological evidence base and framing future research priorities for tick-borne diseases. Using this model we explore the relative contribution of different host groups and transmission routes to the maintenance of a neglected zoonotic tick-borne disease, Kyasanur Forest Disease Virus (KFD), in multiple habitat types. The results highlight the potential importance of transovarial transmission and small mammals and birds in maintaining this disease. This contradicts previous hypotheses that primates play an important role influencing the distribution of infected ticks. There is also a suggestion that risk could vary across different habitat types but currently more research is needed to evaluate this relationship. In light of these results, we outline the key knowledge gaps for this system and future research priorities that could inform effective interventions and control measures.

Ecological niche model transferability of the white star apple (Chrysophyllum albidum G. Don) in the context of climate and global changes

Chrysophyllum albidum is a forest food tree species of the Sapotaceae family bearing large berries of nutrition, sanitary, and commercial value in many African countries. Because of its socioeconomic importance, C. albidum is threatened at least by human pressure. However, we do not know to what extent climate change can impact its distribution or whether it is possible to introduce the species in other tropical regions. To resolve our concerns, we decided to model the spatial distribution of the species. We then used the SDM package for data modeling in R to compare the predictive performances of algorithms among the most commonly used: three machine learning algorithms (MaxEnt, boosted regression trees, and random forests) and three regression algorithms (generalized linear model, generalized additive models, and multivariate adaptive regression spline). We performed model transfers in tropical Asia and Latin America. At the scale of Africa, predictions with respect to Maxent under Africlim (scenarios RCP 4.5 and RCP 8.5, horizon 2055) and MIROCES2L (scenarios SSP245 and SSP585, horizon 2060) showed that the suitable areas of C. albidum, within threshold values of the most contributing variables to the models, will extend mostly in West, East, Central, and Southern Africa as well as in East Madagascar. As opposed to Maxent, in Africa, the predictions for the future of BRT and RF were unrealistic with respect to the known ecology of C. albidum. All the algorithms except Maxent (for tropical Asia only), were consistent in predicting a successful introduction of C. albidum in Latin America and tropical Asia, both at present and in the future. We therefore recommend the introduction and cultivation of Chrysophyllum albidum in the predicted suitable areas of Latin America and tropical Asia, along with vegetation inventories in order to discover likely, sister or vicarious species of Chrysophyllum albidum that can be new to Science. Africlim is more successful than MIROCES2L in predicting realistic suitable areas of Chrysophyllum albidum in Africa. We therefore recommend to the authors of Africlim an update of Africlim models to comply with the sixth Assessment Report (AR6) of IPCC.

Epidemiological Context and Risk Factors Associated with the Evolution of the Coronavirus Disease (COVID-19): A Retrospective Cohort Study

Since its initial appearance in December 2019, COVID-19 has posed a serious challenge to healthcare authorities worldwide. The purpose of the current study was to identify the epidemiological context associated with the respiratory illness propagated by the spread of COVID-19 and outline various risk factors related to its evolution in the province of Debila (Southeastern Algeria). A retrospective analysis was carried out for a cohort of 612 COVID-19 patients admitted to hospitals between March 2020 and February 2022. The results were analyzed using descriptive statistics. Further, logistic regression analysis was employed to perform the odds ratio. In gendered comparison, males were found to have a higher rate of incidence and mortality compared to females. In terms of age, individuals with advanced ages of 60 years or over were typically correlated with higher rates of incidence and mortality in comparison toindividuals below this age. Furthermore, the current research indicated that peri-urban areas were less affected that the urban regions, which had relatively significant incidence and mortality rates. The summer season was marked with the highest incidence and mortality rate in comparison with other seasons. Patients who were hospitalized, were the age of 60 or over, or characterized by comorbidity, were mainly associated with death evolution (odds ratio [OR] = 8.695; p = 0.000), (OR = 6.192; p = 0.000), and (OR = 2.538; p = 0.000), respectively. The study identifies an important relationship between the sanitary status of patients, hospitalization, over-age categories, and the case severity of the COVID-19 patient.

Human-Altered Landscapes and Climate to Predict Human Infectious Disease Hotspots

Background: Zoonotic diseases account for more than 70% of emerging infectious diseases (EIDs). Due to their increasing incidence and impact on global health and the economy, the emergence of zoonoses is a major public health challenge. Here, we use a biogeographic approach to predict future hotspots and determine the factors influencing disease emergence. We have focused on the following three viral disease groups of concern: Filoviridae, Coronaviridae, and Henipaviruses. Methods: We modelled presence–absence data in spatially explicit binomial and zero-inflation binomial logistic regressions with and without autoregression. Presence data were extracted from published studies for the three EID groups. Various environmental and demographical rasters were used to explain the distribution of the EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models. Results: For each group of viruses, we were able to identify and map areas at high risk of disease emergence based on the spatial distribution of the disease reservoirs and hosts of the three viral groups. Common influencing factors of disease emergence were climatic covariates (minimum temperature and rainfall) and human-induced land modifications. Conclusions: Using topographical, climatic, and previous disease outbreak reports, we can identify and predict future high-risk areas for disease emergence and their specific underlying human and environmental drivers. We suggest that such a predictive approach to EIDs should be carefully considered in the development of active surveillance systems for pathogen emergence and epidemics at local and global scales.

SARS-CoV-2 and wastewater: What does it mean for non-human primates?

In most of our lifetimes, we have not faced a global pandemic such as the novel coronavirus disease 2019. The world has changed as a result. However, it is not only humans who are affected by a pandemic of this scale. Our closest relatives, the non-human primates (NHPs) who encounter researchers, sanctuary/zoo employees, and tourists, are also potentially at risk of contracting the virus from humans due to similar genetic susceptibility. "Anthropozoonosis"-the transmission of diseases from humans to other species-has occurred historically, resulting in infection of NHPs with human pathogens that have led to disastrous outbreaks. Recent studies have assessed the susceptibility of NHPs and predict that catarrhine primates and some lemurs are potentially highly susceptible to infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. There is accumulating evidence that a new factor to consider with the spread of the virus is fecal-oral transmission. The virus has been detected in the watersheds of countries with underdeveloped infrastructure where raw sewage enters the environment directly without processing. This may expose NHPs, and other animals, to SARS-CoV-2 through wastewater contact. Here, we address these concerns and discuss recent evidence. Overall, we suggest that the risk of transmission of SARS-CoV-2 via wastewater is low. Nonetheless, tracking of viral RNA in wastewater does provide a unique testing approach to help protect NHPs at zoos and wildlife sanctuaries. A One Health approach going forward is perhaps the best way to protect these animals from a novel virus, the same way that we would protect ourselves.

Microbiology laboratories involved in disease and antimicrobial resistance surveillance: Strengths and challenges of the central African states

Laboratory systems have been largely neglected on the margins of health systems in Africa. However, since the 2000s, many African countries have benefited from massive investments to strengthen laboratory capacities through projects fighting priority diseases (HIV/AIDS, tuberculosis, malaria). This review examined the laboratory capacities of the Economic Community of Central African States (ECCAS). Online research using specific terms was carried out. Studies published between 2000 and 2021 on the role of the laboratory in disease and antimicrobial resistance surveillance in the 11 ECCAS countries were considered. The number of human and animal health laboratories meeting international standards was very low in the sub-region. There were only seven International Organization for Standardization (ISO) 15189-accredited human health laboratories, with five in Cameroon and two in Rwanda. There were five high biosafety level (BSL) laboratories (one BSL3 laboratory each in Cameroon, the Central African Republic, Democratic Republic of Congo and the Republic of Congo, and one BSL4 laboratory in Gabon) and three ISO 17025-accredited laboratories in the ECCAS sub-region. Only six countries currently have whole-genome sequencing devices, which is insufficient for a sub-region as large and populous as ECCAS. Yet, a plethora of pathogens, particularly haemorrhagic viruses, are endemic in these countries. The need for laboratory capacity strengthening following a One Health approach is imperative. Since emerging and re-emerging zoonotic infectious diseases are projected to triple in frequency over the next 50 years and given the inextricable link between human and animal health, actors in the two health sectors must collaborate to preserve world health.

Spatiotemporal Variations of Plague Risk in the Tibetan Plateau from 1954-2016

Plague persists in the plague natural foci today. Although previous studies have found climate drives plague dynamics, quantitative analysis on animal plague risk under climate change remains understudied. Here, we analyzed plague dynamics in the Tibetan Plateau (TP) which is a climate-sensitive area and one of the most severe animal plague areas in China to disentangle variations in marmot plague enzootic foci, diffusion patterns, and their possible links with climate and anthropogenic factors. Specifically, we developed a time-sharing ecological niche modelling framework to identify finer potential plague territories and their temporal epidemic trends. Models were conducted by assembling animal records and multi-source ecophysiological variables with actual ecological effects (both climatic predictors and landscape factors) and driven by matching plague strains to periods corresponding to meteorological datasets. The models identified abundant animal plague territories over the TP and suggested the spatial patterns varied spatiotemporal dimension across the years, undergoing repeated spreading and contractions. Plague risk increased in the 1980s and 2000s, with the risk area increasing by 17.7 and 55.5 thousand km², respectively. The 1990s and 2010s were decades of decreased risk, with reductions of 71.9 and 39.5 thousand km², respectively. Further factor analysis showed that intrinsic conditions (i.e., elevation, soil, and geochemical landscape) provided fundamental niches. In contrast, climatic conditions, especially precipitation, led to niche differentiation and resulted in varied spatial patterns. Additionally, while increased human interference may temporarily reduce plague risks, there is a strong possibility of recurrence. This study reshaped the plague distribution at multiple time scales in the TP and revealed multifactorial synergistic effects on the spreading and contraction of plague foci, confirming that TP plague is increasingly sensitive to climate change. These findings may facilitate groups to take measures to combat the plague threats and prevent potential future human plague from occurring.

Detection of SARS-CoV-2 in urban stormwater: An environmental reservoir and potential interface between human and animal sources

While wastewater has been found to harbor SARS-CoV-2, the persistence of SARSCoV-2 in stormwater and potential transmission is poorly understood. It is plausible that the virus is detectable in stormwater samples where human-originated fecal contamination may have occurred from sources like sanitary sewer overflows, leaky wastewater pipes, and non-human animal waste. Because of these potential contamination pathways, it is possible that stormwater could serve as an environmental reservoir and transmission pathway for SARS-CoV-2. The objectives of this study are: 1) determine whether the presence of SARS-CoV-2 could be detected in stormwater via RT-ddPCR (reverse transcription-digital droplet PCR); 2) quantify human-specific fecal contamination using microbial source tracking; and 3) examine whether rainfall characteristics influence virus concentrations. To accomplish these objectives, we investigated whether SARS-CoV-2 could be detected from 10 storm sewer outfalls each draining a single, dominant land use in Columbus, Xenia, and Springboro, Ohio. Of the 25 samples collected in 2020, at minimum one SARS-CoV-2 target gene (N2 [US-CDC and CN-CDC], and E) was detected in 22 samples (88%). A single significant correlation (p = 0.001), between antecedent dry period and the USCDC N2 gene, was found between target gene concentrations and rainfall characteristics. Grouped by city, two significant relationships emerged showing cities had different levels of the SARS-CoV-2 E gene. Given the differences in scale, the county-level COVID-19 confirmed cases COVID-19 rates were not significantly correlated with stormwater outfall-scale SARS-CoV-2 gene concentrations. Countywide COVID-19 data did not accurately portray neighborhood-scale confirmed COVID-19 case rates. Potential hazards may arise when human fecal contamination is present in stormwater and facilitates future investigation on the threat of viral outbreaks via surfaces waters where fecal contamination may have occurred. Future studies should investigate whether humans are able to contract SARS-CoV-2 from surface waters and the factors that may affect viral longevity and transmission.

COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning

This work aims to develop a data driven multi-horizon incidence forecasting model considering the inter-country variability in static socio-economic factors. The specific objectives of this study are to predict the future country-wise COVID 19 incidences, to locate the influences of individual socio-economic factors on the predictions, to analyze the clusters of countries on the basis of influential explanatory variables and thus to search for intra-cluster and inter-cluster characteristics. To that respect this study has used the deep neural network based temporal fusion transformer for the predictions, Pearson correlation to understand the influence of socio-economic variables on incidence and hierarchical clustering for cluster-analysis. The findings conclude that the inter-country infection related predictions vary widely over spatio-temporal variability and different socio-economic variables have different influences over this inter-country variability. It is observed that greater the population size, stronger the global connectedness, larger the social cohesion, higher the population density and meaningful the gender based discrimination higher will be the future spread. On the other hand greater the development level, higher the nutritional status, greater the access to quality health services, greater the urban population and greater the material poverty lesser will be the future spread. Definite spatial pattern of influence of the explanatory variables emerged from cluster analysis. To minimize the vulnerability towards unforeseen biological calamities modern and sustainable development policies are needed; affluence may not guarantee less infection. But these policies should vary between economies due to the variation in socio-economic status of the countries worldwide.

Regional poverty and infection disease: early exploratory evidence from the COVID-19 pandemic

This paper examines the role of regional poverty on the COVID-19 pandemic in the USA. It also explores how the effects differ with the concentration of ethnic minorities. We find that poverty is a significant and consistent determinant of higher COVID-19 infections and fatalities. Prevalent poverty areas experienced higher infections due to economic structure that require hypermobility (high mobility and interpersonal interaction)-more physical human to human contact resulting in higher deaths from limited access to health services. These are also regions where minority groups are concentrated. Disproportionate infections and fatalities occurred within the black, Hispanic, and Asian population. Our evidence is robust to state fixed effects that capture local COVID-19 mitigation policies, multi-level hierarchical modeling, spatial autoregressive assessment, and large sets of county-level health, social, and economic factors. This paper contributes to the literature on health and economic disparities and their resulting consequences for infectious diseases.

Impact of Climate Change on the Hydrological Regime of the Yarkant River Basin, China: An Assessment Using Three SSP Scenarios of CMIP6 GCMs

Quantification of the impacts of climate change on streamflow and other hydrological parameters is of high importance and remains a challenge in arid areas. This study applied a modified distributed hydrological model (HEC-HMS) to the Yarkant River basin, China to assess hydrological changes under future climate change scenarios. Climate change was assessed based on six CMIP6 general circulation models (GCMs), three shared socio-economic pathways (SSP126, SSP245, SSP370), and several bias correction methods, whereas hydrological regime changes were assessed over two timeframes, referred to as the near future (2021–2049) and the far future (2071–2099). Results demonstrate that the DM (distribution mapping) and LOCI (local intensity scaling) bias correction methods most closely fit the projections of temperature and precipitation, respectively. The climate projections predicted a rise in temperature of 1.72–1.79 °C under the three SSP scenarios for the near future, and 3.76–6.22 °C under the three SSPs for the far future. Precipitation increased by 10.79–12% in the near future, and by 14.82–29.07% during the far future. It is very likely that streamflow will increase during both the near future (10.62–19.2%) and far future (36.69–70.4%) under all three scenarios. The increase in direct flow will be greater than baseflow. Summer and winter streamflow will increase the most, while the increase in streamflow was projected to reach a maximum during June and July over the near future. Over the far future, runoff reached a peak in May and June. The timing of peak streamflow will change from August to July in comparison to historical records. Both high- and low-flow magnitudes during March, April, and May (MAM) as well as June, July, and August (JJA) will increase by varying degrees, whereas the frequency of low flows will decrease during both MAM and JJA. High flow frequency in JJA was projected to decrease. Overall, our results reveal that the hydrological regime of the Yarkant River is likely to change and will be characterized by larger seasonal uncertainty and more frequent extreme events due to significant warming over the two periods. These changes should be seriously considered during policy development.

Challenges in modelling the dynamics of infectious diseases at the wildlife-human interface

The Covid-19 pandemic is of zoonotic origin, and many other emerging infections of humans have their origin in an animal host population. We review the challenges involved in modelling the dynamics of wildlife–human interfaces governing infectious disease emergence and spread. We argue that we need a better understanding of the dynamic nature of such interfaces, the underpinning diversity of pathogens and host–pathogen association networks, and the scales and frequencies at which environmental conditions enable spillover and host shifting from animals to humans to occur. The major drivers of the emergence of zoonoses are anthropogenic, including the global change in climate and land use. These, and other ecological processes pose challenges that must be overcome to counterbalance pandemic risk. The development of more detailed and nuanced models will provide better tools for analysing and understanding infectious disease emergence and spread.

Mapping of Ebola virus spillover: Suitability and seasonal variability at the landscape scale

The unexpected Ebola virus outbreak in West Africa in 2014 involving the Zaire ebolavirus made clear that other regions outside Central Africa, its previously documented niche, were at risk of future epidemics. The complex transmission cycle and a lack of epidemiological data make mapping areas at risk of the disease challenging. We used a Geographic Information System-based multicriteria evaluation (GIS-MCE), a knowledge-based approach, to identify areas suitable for Ebola virus spillover to humans in regions of Guinea, Congo and Gabon where Ebola viruses already emerged. We identified environmental, climatic and anthropogenic risk factors and potential hosts from a literature review. Geographical data layers, representing risk factors, were combined to produce suitability maps of Ebola virus spillover at the landscape scale. Our maps show high spatial and temporal variability in the suitability for Ebola virus spillover at a fine regional scale. Reported spillover events fell in areas of intermediate to high suitability in our maps, and a sensitivity analysis showed that the maps produced were robust. There are still important gaps in our knowledge about what factors are associated with the risk of Ebola virus spillover. As more information becomes available, maps produced using the GIS-MCE approach can be easily updated to improve surveillance and the prevention of future outbreaks.

Ebola virus disease is a highly pathogenic disease transmitted from wildlife to humans. It was first described in 1976 and its distribution remained restricted to Central Africa until 2014, when an outbreak in West Africa, causing more than 28,000 cases and more than 11,000 deaths, took place. Anthropogenic factors, such as bushmeat hunting, trade and consumption, and environmental and climatic factors, may promote the contact between humans and infected animals, such as bats, primates and duikers, increasing the risk of virus transmission to the human population. In this study, we used the spatial multicriteria evaluation framework to gather all available information on risk factors and animal species susceptible to infection, and produce maps of areas suitable for Ebola virus spillover in regions in Guinea, Congo and Gabon. The resulting maps highlighted high spatial and temporal variability in the suitability for Ebola virus spillover. Data from reported cases of Ebola virus transmission from wild animals to humans were used to validate the maps. The approach developed is capable of integrating a wide diversity of risk factors, and provides a flexible and simple tool for surveillance, which can be updated as more data and knowledge on risk factors become available.

Ultrasensitive point-of-care immunoassay for secreted glycoprotein detects Ebola infection earlier than PCR

Ebola virus (EBOV) hemorrhagic fever outbreaks have been challenging to deter due to the lack of health care infrastructure in disease-endemic countries and a corresponding inability to diagnose and contain the disease at an early stage. EBOV vaccines and therapies have improved disease outcomes, but the advent of an affordable, easily accessed, mass-produced rapid diagnostic test (RDT) that matches the performance of more resource-intensive polymerase chain reaction (PCR) assays would be invaluable in containing future outbreaks. Here, we developed and demonstrated the performance of a new ultrasensitive point-of-care immunoassay, the EBOV D4 assay, which targets the secreted glycoprotein of EBOV. The EBOV D4 assay is 1000-fold more sensitive than the U.S. Food and Drug Administration-approved RDTs and detected EBOV infection earlier than PCR in a standard nonhuman primate model. The EBOV D4 assay is suitable for low-resource settings and may facilitate earlier detection, containment, and treatment during outbreaks of the disease.

Towards an ecosystem model of infectious disease

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics.

The effect of human settlement temperature and humidity on the growth rules of infected and recovered cases of COVID-19

This study investigated the impact of humidity and temperature on the spread of COVID-19 (SARS-CoV-2) by statistically comparing modelled pandemic dynamics (daily infection and recovery cases) with daily temperature and humidity of three climate zones (Mainland China, South America and Africa) from January to August 2020. We modelled the pandemic growth using a simple logistic function to derive information of the viral infection and describe the growth of infected and recovered cases. The results indicate that the infected and recovered cases of the first wave were controlled in China and managed in both South America and Africa. There is a negative correlation between both humidity (r = - 0.21; p = 0.27) and temperature (r = -0.22; p = 0.24) with spread of the virus. Though this study did not fully encompass socio-cultural factors, we recognise that local government responses, general health policies, population density and transportation could also affect the spread of the virus. The pandemic can be managed better in the second wave if stricter safety protocols are implemented. We urge various units to collaborate strongly and call on countries to adhere to stronger safety protocols in the second wave.

Gaps in health security related to wildlife and environment affecting pandemic prevention and preparedness, 2007-2020

OBJECTIVE

To describe and quantify the extent of wildlife and environment sector inclusion in country evaluation and prioritization tools for health security, and to provide practical recommendations for global and national action to improve pandemic prevention and preparedness.

METHODS

To assess coverage of wildlife and other environmental aspects, we reviewed major health security reports (including World Organisation for Animal Health Performance of Veterinary Services reports, and World Health Organization Joint External Evaluations and follow-on National Action Plans for Health Security) published by 107 countries and territories. We extracted information on stated coverage gaps, wildlife surveillance systems and priority diseases. We also searched National Biodiversity Strategies and Action Plans published by 125 countries to assess whether disease surveillance or prevention activities were included.

FINDINGS

We noted that the occurrence frequency of keywords indicative of wildlife, environment, biodiversity and climate factors varied with type of report and between countries. We found that more than half (57.9%, 62/107) of the reporting countries did not provide any evidence of a functional wildlife health surveillance programme. Most countries (83.2%, 89/107) indicated specific gaps in operations, coordination, scope or capacity. Only eight of the 125 countries (6.4%) publishing a National Biodiversity Strategy and Action Plan reported tangible activities related to wildlife health or zoonotic disease.

CONCLUSION

Overall, despite their importance for pandemic prevention, wildlife and environmental considerations are neglected in health security priorities and plans. Strengthening wildlife health capacity and operations should be emphasized in One Health efforts to monitor and mitigate known and novel disease risks.

Data quantity is more important than its spatial bias for predictive species distribution modelling

Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of (1) spatial bias in training data, (2) sample size (the average number of observations per species), and (3) the choice of species distribution modelling method. Our approach is novel in quantifying and applying real-world spatial sampling biases to simulated data. Spatial bias in training data decreased species distribution model prediction performance, but sample size and the choice of modelling method were more important than spatial bias in determining the prediction performance of species distribution models.

Risk factors for Ebola exposure in healthcare workers in Boende, Tshuapa Province, Democratic Republic of the Congo

Healthcare workers (HCW) are more likely to be exposed to Ebola virus (EBOV) during an outbreak compared to people in the general population due to close physical contact with patients and potential exposure to infectious fluids. However, not all will fall ill. Despite evidence of subclinical and paucisymptomatic Ebola Virus Disease (EVD), the prevalence and associated risk factors remains unknown. We conducted a serosurvey among healthcare workers in the town of Boende in Tshuapa Province, Democratic Republic of Congo (DRC). Human anti-EBOV Glycoprotein (GP) IgG titers were measured using a commercially available ELISA kit. We assessed associations between anti-EBOV IgG seroreactivity, defined as ≥2.5 units/mL and risk factors using univariable and multivariable logistic regression. Sensitivity analyses explored a more conservative cutoff >5 units/mL. Overall, 22.5% of HCWs were seroreactive for EBOV. In multivariable analyses, using any form of personal protective equipment (PPE) when interacting with a confirmed, probable, or suspect EVD case was negatively associated with seroreactivity [0.23 (95% CI: 0.07, 0.73)]. Our results suggest high exposure to EBOV among HCWs and provide additional evidence for asymptomatic or minimally symptomatic EVD. Further studies should be conducted to determine the probability of onward transmission and if seroreactivity is associated with immunity.

Prediction of the COVID-19 spread in African countries and implications for prevention and control: A case study in South Africa, Egypt, Algeria, Nigeria, Senegal and Kenya

COVID-19 (Corona Virus Disease 2019) is globally spreading and the international cooperation is urgently required in joint prevention and control of the epidemic. Using the Maximum-Hasting (MH) parameter estimation method and the modified Susceptible Exposed Infectious Recovered (SEIR) model, the spread of the epidemic under three intervention scenarios (suppression, mitigation, mildness) is simulated and predicted in South Africa, Egypt, and Algeria, where the epidemic situations are severe. The studies are also conducted in Nigeria, Senegal and Kenya, where the epidemic situations are growing rapidly and the socio-economic are relatively under-developed, resulting in more difficulties in preventing the epidemic. Results indicated that the epidemic can be basically controlled in late April with strict control of scenario one, manifested by the circumstance in the South Africa and Senegal. Under moderate control of scenario two, the number of infected people will increase by 1.43-1.55 times of that in scenario one, the date of the epidemic being controlled will be delayed by about 10 days, and Algeria, Nigeria, and Kenya are in accordance with this situation. In the third scenario of weak control, the epidemic will be controlled by late May, the total number of infected cases will double that in scenario two, and Egypt is in line with this prediction. In the end, a series of epidemic controlling methods are proposed, including patient quarantine, close contact tracing, population movement control, government intervention, city and county epidemic risk level classification, and medical cooperation and the Chinese assistance.

Confronting the Upstream Causes of COVID-19 and Other Epidemics to Follow

The upstream causes of the COVID-19 pandemic have received little attention so far in public health and clinical medicine, as opposed to the downstream effects of mass morbidity and mortality. To resolve this pandemic and to prevent even more severe future pandemics, a focus on upstream causation is essential. Convincing evidence shows that this and every other important viral epidemic emerging in the recent past and predictably into the future comes from the same upstream causes: capitalist agriculture, its destruction of natural habitat, and the industrial production of meat. International and national health organizations have obscured the upstream causes of emerging viral epidemics. These organizations have suffered cutbacks in public funding but have received increased support from international financial institutions and private philanthropies that emphasize the downstream effects rather than upstream causes of infectious diseases. Conflicts of interest also have impacted public health policies. A worldwide shift has begun toward peasant agricultural practices: Research so far has shown that peasant agriculture is safer and more efficient than capitalist industrial agricultural practices. Without such a transformation of agriculture, even more devastating pandemics will result from the same upstream causes.

Is COVID-19 a Message from Nature?

Claims have been made that the current COVID-19 pandemic is a message from nature to stop exploiting the earth to the extent that we have been. While there is no direct evidence that this pandemic is a result of human actions with respect to the earth, ample evidence exists that deforestation and other environmental changes, together with climate change, do make it more likely that viruses will cross from wildlife to humans. We humans are mammals and our welfare depends on the health of the earth. We are not so different from other living creatures in this regard. It is in our interests to look after the earth, something that Indigenous Australians knew well. Mother Earth must be cared for if she is to care for us. Nature perhaps is sending us a message in the same sense that my car does if I do not maintain it. It stops functioning properly. We have to modify nature to satisfy our needs but we must be careful how we modify it.