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1. Pandemic-associated mobility restrictions could cause increases in dengue virus transmission

   https://doi.org/10.1371/journal.pntd.0009603  

   Cavany, Sean M. ; España, Guido ; Vazquez-Prokopec, Gonzalo M. ; Scott, Thomas W. ; Perkins, T Alex ( August 2021 , PLOS Neglected Tropical Diseases)

   Christofferson, Rebecca C. (Ed.)

   Background The COVID-19 pandemic has induced unprecedented reductions in human mobility and social contacts throughout the world. Because dengue virus (DENV) transmission is strongly driven by human mobility, behavioral changes associated with the pandemic have been hypothesized to impact dengue incidence. By discouraging human contact, COVID-19 control measures have also disrupted dengue vector control interventions, the most effective of which require entry into homes. We sought to investigate how and why dengue incidence could differ under a lockdown scenario with a proportion of the population sheltered at home. Methodology & principal findings We used an agent-based model with a realistic treatment of human mobility and vector control. We found that a lockdown in which 70% of the population sheltered at home and which occurred in a season when a new serotype invaded could lead to a small average increase in cumulative DENV infections of up to 10%, depending on the time of year lockdown occurred. Lockdown had a more pronounced effect on the spatial distribution of DENV infections, with higher incidence under lockdown in regions with higher mosquito abundance. Transmission was also more focused in homes following lockdown. The proportion of people infected in their own home rose from 54% under normal conditions to 66% under lockdown, and the household secondary attack rate rose from 0.109 to 0.128, a 17% increase. When we considered that lockdown measures could disrupt regular, city-wide vector control campaigns, the increase in incidence was more pronounced than with lockdown alone, especially if lockdown occurred at the optimal time for vector control. Conclusions & significance Our results indicate that an unintended outcome of lockdown measures may be to adversely alter the epidemiology of dengue. This observation has important implications for an improved understanding of dengue epidemiology and effective application of dengue vector control. When coordinating public health responses during a syndemic, it is important to monitor multiple infections and understand that an intervention against one disease may exacerbate another. 

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2. Situating Engineering Education in a World Impacted by COVID-19

   De Pree, Thomas ; Appelhans, Sarah ; Cheville, Alan ; Akera, Atsushi ; Shuey, Melissa ( July 2021 , ASEE annual conference exposition)

   null (Ed.)

   In June 2020, at the annual conference of the American Society for Engineering Education (ASEE), which was held entirely online due to the impacts of COVID-19 (SARS-CoV-2), engineering education researchers and social justice scholars diagnosed the spread of two diseases in the United States: COVID-19 and racism. During a virtual workshop (T614A) titled, “Using Power, Privilege, and Intersectionality as Lenses to Understand our Experiences and Begin to Disrupt and Dismantle Oppressive Structures Within Academia,” Drs. Nadia Kellam, Vanessa Svihla, Donna Riley, Alice Pawley, Kelly Cross, Susannah Davis, and Jay Pembridge presented what we might call a pathological analysis of institutionalized racism and various other “isms.” In order to address the intersecting impacts of this double pandemic, they prescribed counter practices and protocols of anti-racism, and strategies against other oppressive “isms” in academia. At the beginning of the virtual workshop, the presenters were pleasantly surprised to see that they had around a hundred attendees. Did the online format of the ASEE conference afford broader exposure of the workshop? Did recent uprising of Black Lives Matter (BLM) protests across the country, and internationally, generate broader interest in their topic? Whatever the case, at a time when an in-person conference could not be convened without compromising public health safety, ASEE’s virtual conference platform, furnished by Pathable and supplemented by Zoom, made possible the broader social impacts of Dr. Svihla’s land acknowledgement of the unceded Indigenous lands from which she was presenting. Svihla attempted to go beyond a hollow gesture by including a hyperlink in her slides to a COVID-19 relief fund for the Navajo Nation, and encouraged attendees to make a donation as they copied and pasted the link in the Zoom Chat. Dr. Cross’s statement that you are either a racist or an anti-racist at this point also promised broader social impacts in the context of the virtual workshop. You could feel the intensity of the BLM social movements and the broader political climate in the tone of the presenters’ voices. The mobilizing masses on the streets resonated with a cutting-edge of social justice research and education at the ASEE virtual conference. COVID-19 has both exacerbated and made more obvious the unevenness and inequities in our educational practices, processes, and infrastructures. This paper is an extension of a broader collaborative research project that accounts for how an exceptional group of engineering educators have taken this opportunity to socially broaden their curricula to include not just public health matters, but also contemporary political and social movements. Engineering educators for change and advocates for social justice quickly recognized the affordances of diverse forms of digital technologies, and the possibilities of broadening their impact through educational practices and infrastructures of inclusion, openness, and accessibility. They are makers of what Gary Downy calls “scalable scholarship”—projects in support of marginalized epistemologies that can be scaled up from ideation to practice in ways that unsettle and displace the dominant epistemological paradigm of engineering education.[1] This paper is a work in progress. It marks the beginning of a much lengthier project that documents the key positionality of engineering educators for change, and how they are socially situated in places where they can connect social movements with industrial transitions, and participate in the production of “undone sciences” that address “a structured absence that emerges from relations of inequality.”[2] In this paper, we offer a brief glimpse into ethnographic data we collected virtually through interviews, participant observation, and digital archiving from March 2019 to August 2019, during the initial impacts of COVID-19 in the United States. The collaborative research that undergirds this paper is ongoing, and what is presented here is a rough and early articulation of ideas and research findings that have begun to emerge through our engagement with engineering educators for change. This paper begins by introducing an image concept that will guide our analysis of how, in this historical moment, forms of social and racial justice are finding their way into the practices of engineering educators through slight changes in pedagogical techniques in response the debilitating impacts of the pandemic. Conceptually, we are interested in how small and subtle changes in learning conditions can socially broaden the impact of engineering educators for change. After introducing the image concept that guides this work, we will briefly discuss methodology and offer background information about the project. Next, we discuss literature that revolves around the question, what is engineering education for? Finally, we introduce the notion of situating engineering education and give readers a brief glimpse into our ethnographic data. The conclusion will indicate future directions for writing, research, and intervention. 

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3. Impact of public sentiments on the transmission of COVID-19 across a geographical gradient

   https://doi.org/10.7717/peerj.14736  

   Agusto, Folashade B. ; Numfor, Eric ; Srinivasan, Karthik ; Iboi, Enahoro A. ; Fulk, Alexander ; Saint Onge, Jarron M. ; Peterson, A. Townsend ( January 2023 , PeerJ)

   COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual’s belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people’s sentiments (positive and negative) which accounts for the influence of disinformation. People’s sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19. 

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4. 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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5. Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders

   https://doi.org/10.2196/45757  

   Xia, Xinming ; Zhang, Yi ; Jiang, Wenting ; Wu, Connor Yuhao ( January 2023 , Journal of Medical Internet Research)

   Stay-at-home orders were one of the controversial interventions to curb the spread of COVID-19 in the United States. The stay-at-home orders, implemented in 51 states and territories between March 7 and June 30, 2020, impacted the lives of individuals and communities and accelerated the heavy usage of web-based social networking sites. Twitter sentiment analysis can provide valuable insight into public health emergency response measures and allow for better formulation and timing of future public health measures to be released in response to future public health emergencies.

   This study evaluated how stay-at-home orders affect Twitter sentiment in the United States. Furthermore, this study aimed to understand the feedback on stay-at-home orders from groups with different circumstances and backgrounds. In addition, we particularly focused on vulnerable groups, including older people groups with underlying medical conditions, small and medium enterprises, and low-income groups.

   We constructed a multiperiod difference-in-differences regression model based on the Twitter sentiment geographical index quantified from 7.4 billion geo-tagged tweets data to analyze the dynamics of sentiment feedback on stay-at-home orders across the United States. In addition, we used moderated effects analysis to assess differential feedback from vulnerable groups.

   We combed through the implementation of stay-at-home orders, Twitter sentiment geographical index, and the number of confirmed cases and deaths in 51 US states and territories. We identified trend changes in public sentiment before and after the stay-at-home orders. Regression results showed that stay-at-home orders generated a positive response, contributing to a recovery in Twitter sentiment. However, vulnerable groups faced greater shocks and hardships during the COVID-19 pandemic. In addition, economic and demographic characteristics had a significant moderating effect.

   This study showed a clear positive shift in public opinion about COVID-19, with this positive impact occurring primarily after stay-at-home orders. However, this positive sentiment is time-limited, with 14 days later allowing people to be more influenced by the status quo and trends, so feedback on the stay-at-home orders is no longer positively significant. In particular, negative sentiment is more likely to be generated in states with a large proportion of vulnerable groups, and the policy plays a limited role. The pandemic hit older people, those with underlying diseases, and small and medium enterprises directly but hurt states with cross-cutting economic situations and more complex demographics over time. Based on large-scale Twitter data, this sociological perspective allows us to monitor the evolution of public opinion more directly, assess the impact of social events on public opinion, and understand the heterogeneity in the face of pandemic shocks.

    

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