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1. Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platform

   https://doi.org/10.1186/s40537-021-00423-z  

   Villanustre, Flavio ; Chala, Arjuna ; Dev, Roger ; Xu, Lili ; LexisNexis, Jesse Shaw ; Furht, Borko ; Khoshgoftaar, Taghi ( December 2021 , Journal of Big Data)

   Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper. 

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2. Dispersion of sneeze droplets in a meat facility indoor environment – Without partitions

   https://doi.org/10.1016/j.envres.2023.116603  

   Kumar, Sunil ; Klassen, Mark ; Klassen, David ; Hardin, Robert ; King, Maria D. ( November 2023 , Environmental Research)

   Jose L. Domingo (Ed.)

   Spreading patterns of the coronavirus disease (COVID-19) showed that infected and asymptotic carriers both played critical role in escalating transmission of virus leading to global pandemic. Indoor environments of res- taurants, classrooms, hospitals, offices, large assemblies, and industrial installations are susceptible to virus outbreak. Industrial facilities such as fabrication rooms of meat processing plants, which are laden with moisture and fat in indoor air are the most sensitive spaces. Fabrication room workers standing next to each other are exposed to the risk of long-range viral droplets transmission within the facility. An asymptomatic carrier may transmit the virus unintentionally to fellow workers through sporadic sneezing leading to community spread. A novel Computational Fluid Dynamics (CFD) model of a fabrication room with typical interior (stationary objects) was prepared and investigated. Study was conducted to identify indoor airflow patterns, droplets spreading patterns, leading droplets removal mechanism, locations causing maximum spread of droplets, and infection index for workers along with stationary objects in reference to seven sneeze locations covering the entire room. The role of condensers, exhaust fans and leakage of indoor air through large and small openings to other rooms was investigated. This comprehensive study presents flow scenarios in the facility and helps identify locations that are potentially at lower or higher risk for exposure to COVID-19. The results presented in this study are suitable for future engineering analyses aimed at redesigning public spaces and common areas to minimize the spread of aerosols and droplets that may contain pathogens. 

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3. Salinity stress increases the severity of ranavirus epidemics in amphibian populations

   https://doi.org/10.1098/rspb.2020.0062  

   Hall, Emily M. ; Brunner, Jesse L. ; Hutzenbiler, Brandon ; Crespi, Erica J. ( May 2020 , Proceedings of the Royal Society B: Biological Sciences)

   The stress-induced susceptibility hypothesis, which predicts chronic stress weakens immune defences, was proposed to explain increasing infectious disease-related mass mortality and population declines. Previous work characterized wetland salinization as a chronic stressor to larval amphibian populations. Thus, we combined field observations with experimental exposures quantifying epidemiological parameters to test the role of salinity stress in the occurrence of ranavirus-associated mass mortality events. Despite ubiquitous pathogen presence (94%), populations exposed to salt runoff had slightly more frequent ranavirus related mass mortality events, more lethal infections, and 117-times greater pathogen environmental DNA. Experimental exposure to chronic elevated salinity (0.8–1.6 g l −1 Cl − ) reduced tolerance to infection, causing greater mortality at lower doses. We found a strong negative relationship between splenocyte proliferation and corticosterone in ranavirus-infected larvae at a moderate elevation of salinity, supporting glucocorticoid-medicated immunosuppression, but not at high salinity. Salinity alone reduced proliferation further at similar corticosterone levels and infection intensities. Finally, larvae raised in elevated salinity had 10 times more intense infections and shed five times as much virus with similar viral decay rates, suggesting increased transmission. Our findings illustrate how a small change in habitat quality leads to more lethal infections and potentially greater transmission efficiency, increasing the severity of ranavirus epidemics. 

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4. The dynamics of evolutionary rescue from a novel pathogen threat in a host metapopulation

   https://doi.org/10.1038/s41598-021-90407-z  

   Jiao, Jing ; Fefferman, Nina ( December 2021 , Scientific Reports)

   Abstract When a novel disease strikes a naïve host population, there is evidence that the most immediate response can involve host evolution while the pathogen remains relatively unchanged. When hosts also live in metapopulations, there may be critical differences in the dynamics that emerge from the synergy among evolutionary, ecological, and epidemiological factors. Here we used a Susceptible-Infected-Recovery model to explore how spatial and temporal ecological factors may drive the epidemiological and rapid-evolutionary dynamics of host metapopulations. For simplicity, we assumed two host genotypes: wild type, which has a positive intrinsic growth rate in the absence of disease, and robust type, which is less likely to catch the infection given exposure but has a lower intrinsic growth rate in the absence of infection. We found that the robust-type host would be strongly selected for in the presence of disease when transmission differences between the two types is large. The growth rate of the wild type had dual but opposite effects on host composition: a smaller increase in wild-type growth increased wild-type competition and lead to periodical disease outbreaks over the first generations after pathogen introduction, while larger growth increased disease by providing more susceptibles, which increased robust host density but decreased periodical outbreaks. Increased migration had a similar impact as the increased differential susceptibility, both of which led to an increase in robust hosts and a decrease in periodical outbreaks. Our study provided a comprehensive understanding of the combined effects among migration, disease epidemiology, and host demography on host evolution with an unchanging pathogen. The findings have important implications for wildlife conservation and zoonotic disease control. 

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5. Linking Lyme disease ecology and epidemiology: reservoir host identity, not richness, determines tick infection and human disease in California

   https://doi.org/10.1088/1748-9326/ac9ece  

   MacDonald, Andrew J ; McComb, Sofie ; Sambado, Samantha ( November 2022 , Environmental Research Letters)

   Abstract Understanding the community ecology of vector-borne and zoonotic diseases, and how it may shift transmission risk as it responds to environmental change, has become a central focus in disease ecology. Yet, it has been challenging to link the ecology of disease with reported human incidence. Here, we bridge the gap between local-scale community ecology and large-scale disease epidemiology, drawing from a priori knowledge of tick-pathogen-host ecology to model spatially-explicit Lyme disease (LD) risk, and human Lyme disease incidence (LDI) in California. We first use a species distribution modeling approach to model disease risk with variables capturing climate, vegetation, and ecology of key reservoir host species, and host species richness. We then use our modeled disease risk to predict human disease incidence at the zip code level across California. Our results suggest the ecology of key reservoir hosts—particularly dusky-footed woodrats—is central to disease risk posed by ticks, but that host community richness is not strongly associated with tick infection. Predicted disease risk, which is most strongly influenced by the ecology of dusky-footed woodrats, in turn is a strong predictor of human LDI. This relationship holds in the Wildland-Urban Interface, but not in open access public lands, and is stronger in northern California than in the state as a whole. This suggests peridomestic exposure to infected ticks may be more important to LD epidemiology in California than recreational exposure, and underlines the importance of the community ecology of LD in determining human transmission risk throughout this LD endemic region of far western North America. More targeted tick and pathogen surveillance, coupled with studies of human and tick behavior could improve understanding of key risk factors and inform public health interventions. Moreover, longitudinal surveillance data could further improve forecasts of disease risk in response to global environmental change. 

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