Tracking COVID-19 using online search

Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest -- as opposed to infections -- using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2 - 23.2) and 22.1 (17.4 - 26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.Comment: Published in Nature Digital Medicine. Please note that the published version differs from this preprin

The UK needs a sustainable strategy for COVID-19

The UK is well into the second wave of COVID-19, with 60 051 lives lost to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection to date, according to provisional data from the&nbsp;Office for National Statistics. Official UK Government&nbsp;data&nbsp;show that cases have been rising exponentially since late August, 2020, with increases across all regions in England in recent weeks. &nbsp;As of Nov 4, 2020, the UK had 25 177 confirmed daily cases. These are almost certainly underestimates as between Oct 17 and Oct 23, 2020, England alone had 52 000 estimated daily cases. &nbsp;Estimates of the effective reproduction number in England vary between 1·1 and 1·6.</p

MERS in the Kingdom of Saudi Arabia : insights from publicly available data

Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2015."September 2015." Cataloged from PDF version of thesis.Includes bibliographical references (pages 18-22).Since 2012, more than 1300 cases of Middle East respiratory syndrome (MERS) have been diagnosed worldwide, the vast majority of which have occurred in Saudi Arabia and over 40% of which have ended in death. In Spring 2014, a large outbreak of MERS originated in the Kingdom of Saudi Arabia - concentrated in nosocomial settings in Riyadh and Jeddah - resulting in over 300 infections. We used publicly available data from the Saudi Ministry of Health and World Health Organization to examine the outbreak potential of MERS-Coronavirus and to explore possible risk factors for MERS-related mortality within the context of Saudi Arabia. We also investigated how differential case characteristics between patients reported during the Spring 2014 Saudi MERS outbreak and those reported during non-outbreak periods may provide insight into the propagation of future outbreaks. We found that the Spring 2014 Saudi MERS outbreak was likely due to a super-spreading event, in which a small fraction of cases caused the vast majority of secondary transmissions. Though most cases infected 1 or fewer other individuals, propensity for super-spreading suggests that the outbreak potential of MERS-Coronavirus is significant and that future outbreaks of similar size are expected to occur. Furthermore, we found that early administration of supportive care may be essential to survival once an individual is infected with MERS-Coronavirus; this is especially true for the elderly, who are at increased risk of death. Thus, surveillance - especially among the elderly, who are at increased risk for MERS-related death - is key to reducing fatality. Surveillance is also integral to detecting zoonotic introduction (i.e. host-to-human transmission) events that may trigger future outbreaks if left uncontained. Finally, we found that female and non-comorbid individuals were preferentially infected during the Spring 2014 outbreak, which may lend insight into the enabling conditions that are necessary for MERS outbreaks to emerge and propagate. Further exploration of the mechanisms that result in the zoonotic introduction of MERS-Coronavirus into the human population - as well as the emergence and propagation of MERS outbreaks - is crucial. As demonstrated by the steady stream of sporadic cases that have been reported since the Spring 2014 outbreak, MERS has already gained a firm foothold in the Kingdom of Saudi Arabia. Given that Saudi Arabia is a universal religious travel destination, localized outbreaks may have massive global implications. Because of this, we conclude with the recommendation that the Saudi government should immediately prioritize systematic outbreak planning, preparedness, and prevention. Developing an early warning system (EWS) for MERS in Saudi Arabia using engineering systems modeling methods - namely, system dynamics - may help achieve these ends. If successfully within the context of MERS-Coronavirus in Saudi Arabia, such a modeling framework may also be generalized to other zoonotic pathogens with similar emergent properties and global ramifications.by Maimuna S. Majumder.S.M. in Engineering System

Modeling transmission heterogeneity for infectious disease outbreaks

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018.Cataloged from PDF version of thesis.Includes bibliographical references.The transmissibility of a given infectious disease is often described by its basic reproduction number (Ro) - namely, the average number of secondary infections caused by an index case in a fully susceptible population. Typical approaches to modeling transmission dynamics associated with infectious disease outbreaks frequently use Ro to produce deterministic case count projections, in effect treating the affected population as homogeneous (i.e. as if every individual in the population interest has an equal likelihood of passing on the infection of interest). As a result, such approaches often fail to effectively capture transmission dynamics during real-world outbreaks in heterogeneous populations. Here, we use analytical and simulation methods to show that the treatment of Ro as the mean of a random variable (thus permitting the estimation of non-deterministic case count projections) allows us to better assess outbreak trajectory and likelihood of disease propagation in non-homogeneous populations (Chapter 2). We then empirically investigate predictors of in-population transmission heterogeneity (i.e. the fact that some individuals in a given population are more likely than others to pass on the infection of interest) within the context of Middle East Respiratory Syndrome in South Korea using a combination of statistical- and review-driven approaches (Chapter 3). Then, in Chapter 4, we explore how in-population transmission heterogeneity can be used to our advantage through the deployment of risk-informed interventions (i.e. in which individuals who are more likely to pass on the infection of interest are exclusively targeted to receive the intervention) during infectious disease outbreaks. More specifically, we use the analytical and simulation methods first introduced in Chapter 2 - paired with inpopulation transmission heterogeneity data from Chapter 3 - to compare the utility of a variance-informed deployment scheme against a traditional, uniform deployment scheme (i.e. in which every individual has an equal likelihood of receiving the intervention). Finally, building off of our findings in Chapters 2, 3, and 4, we recommend four interrelated policies in Chapter 5 that aim to (1) normalize the treatment and reporting of Ro as the mean of a random variable and (2) improve access to the data required to sufficiently capture population heterogeneity when modeling disease propagation.by Maimuna Shahnaz Majumder.Ph. D. in Engineering System

Digital phenotyping of complex psychological responses to the COVID-19 pandemic

Background: The novel coronavirus disease 2019 (COVID-19) has negatively impacted mortality, economic conditions, and mental health. A large scale study on psychological reactions to the pandemic to inform ongoing population-level symptom tracking and response to treatment is currently lacking. Methods: Average intake scores for standard depression and anxiety symptom scales were tracked from January 1, 2017 to June 9, 2020 for patients seeking treatment from a digital mental health service to gauge the relationship between COVID-19 and self-reported symptoms. We applied natural language processing (NLP) to unstructured therapy transcript data from patients seeking treatment during the height of the pandemic in the United States between March 1, 2020 and June 9, 2020 to identify words associated with COVID-19 mentions. This analysis was used to identify symptoms that were present beyond those assessed by standard depression and anxiety measures. Results: Depression and anxiety symptoms reported by 169,889 patients between January 1, 2017 and June 9, 2020 were identified. There was no detectable change in intake depression symptom scores. Intake anxiety symptom scores increased 1.42 scale points [95% CI: 1.18, 1.65] between March 15, 2020 and April 1, 2020, when scores peaked. In the transcript data of these 169,889 patients, plus an expanded sample of 49,267 patients without symptom reports, term frequency-inverse document frequency (tf-idf) identified 2,377 positively correlated and 661 negatively correlated terms that were significantly (FDR&lt;.01) associated with mentions of the virus. These terms were classifiable into 24 symptoms beyond those included in the diagnostic criteria for anxiety or depression. Conclusions: The COVID-19 pandemic may have increased intake anxiety symptoms for individuals seeking digital mental health treatment. NLP analyses suggest that standard symptom scales for depression and anxiety alone are inadequate to fully assess and track psychological reactions to the pandemic. Symptoms of grief, trauma, obsession-compulsion, agoraphobia, hypochondriasis, panic, and non- suicidal self-injury should be monitored as part of a new COVID-19 Syndrome category

Mortality Risk Factors for Middle East Respiratory Syndrome Outbreak, South Korea, 2015

As of July 15, 2015, the South Korean Ministry of Health and Welfare had reported 186 case-patients with Middle East respiratory syndrome in South Korea. For 159 case-patients with known outcomes and complete case histories, we found that older age and preexisting concurrent health conditions were risk factors for death

Machine Learning Maps Research Needs in COVID-19 Literature

As of August 2020, thousands of COVID-19 (coronavirus disease 2019) publications have been produced. Manual assessment of their scope is an overwhelming task, and shortcuts through metadata analysis (e.g., keywords) assume that studies are properly tagged. However, machine learning approaches can rapidly survey the actual text of publication abstracts to identify research overlap between COVID-19 and other coronaviruses, research hotspots, and areas warranting exploration. We propose a fast, scalable, and reusable framework to parse novel disease literature. When applied to the COVID-19 Open Research Dataset (CORD-19), dimensionality reduction suggests that COVID-19 studies to date are primarily clinical-, modeling- or field-based, in contrast to the vast quantity of laboratory-driven research for other (non-COVID-19) coronavirus diseases. Furthermore, topic modeling indicates that COVID-19 publications have focused on public health, outbreak reporting, clinical care, and testing for coronaviruses, as opposed to the more limited number focused on basic microbiology, including pathogenesis and transmission.</p