Person: Buckee, Caroline
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Buckee
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Caroline
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Buckee, Caroline
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Publication Modeling the Comparative Impact of Individual Quarantine vs. Active Monitoring of Contacts for the Mitigation of COVID-19(2020-03-08) Peak, Corey; Kahn, Rebecca; Grad, Yonatan; Childs, Lauren; Li, Ruoran; Lipsitch, Marc; Buckee, CarolineIndividual quarantine and active monitoring of contacts are core disease control strategies, particularly for emerging infectious diseases such as Coronavirus Disease 2019 (COVID-19). To estimate the comparative efficacy of these interventions to control COVID-19, we fit a stochastic branching model, comparing two sets of reported parameters for the dynamics of the disease. Our results suggest that individual quarantine may contain an outbreak of COVID-19 with a short serial interval (4.8 days) only in settings with high intervention performance where at least three-quarters of infected contacts are individually quarantined. However, in settings where this performance is unrealistically high and the outbreak of COVID-19 continues to grow, so too will the burden of the number of contacts traced for active monitoring or quarantine. In such circumstances where resources are prioritized for scalable interventions such as social distancing, we show active monitoring or individual quarantine of high-risk contacts can contribute synergistically to social distancing. To the extent that interventions based on contact tracing can be implemented, therefore, they can help mitigate the spread of COVID-19. Our model highlights the urgent need for more data on the serial interval and the extent of presymptomatic transmission in order to make data-driven policy decisions regarding the cost-benefit comparisons of individual quarantine vs. active monitoring of contacts.Publication U.S. county-level characteristics to inform equitable COVID-19 response(Cold Spring Harbor Laboratory, 2020-04-11) Chin, Taylor; Kahn, Rebecca; Li, Ruoran; Chen, Jarvis; Krieger, Nancy; Buckee, Caroline; Balsari, Satchit; Kiang, MathewBackground: The spread of Coronavirus Disease 2019 (COVID-19) across the United States confirms that not all Americans are equally at risk of infection, severe disease, or mortality. A range of intersecting biological, demographic, and socioeconomic factors are likely to determine an individual’s susceptibility to COVID-19. These factors vary significantly across counties in the United States, and often reflect the structural inequities in our society. Recognizing this vast inter-county variation in risks will be critical to mounting an adequate response strategy. Methods and Findings: Using publicly available county-specific data we identified key biological, demographic, and socioeconomic factors influencing susceptibility to COVID-19, guided by international experiences and consideration of epidemiological parameters of importance. We created bivariate county-level maps to summarize examples of key relationships across these categories, grouping age and poverty; comorbidities and lack of health insurance; proximity, density and bed capacity; and race and ethnicity, and premature death. We have also made available an interactive online tool that allows public health officials to query risk factors most relevant to their local context. Our data demonstrate significant inter-county variation in key epidemiological risk factors, with a clustering of counties in certain states, which will result in an increased demand on their public health system. While the East and West coast cities are particularly vulnerable owing to their densities (and travel routes), a large number of counties in the Southeastern states have a high proportion of at-risk populations, with high levels of poverty, comorbidities, and premature death at baseline, and low levels of health insurance coverage. The list of variables we have examined is by no means comprehensive, and several of them are interrelated and magnify underlying vulnerabilities. The online tool allows readers to explore additional combinations of risk factors, set categorical thresholds for each covariate, and filter counties above different population thresholds. Conclusion: COVID-19 responses and decision making in the United States remain decentralized. Both the federal and state governments will benefit from recognizing high intra-state, inter-county variation in population risks and response capacity. Many of the factors that are likely to exacerbate the burden of COVID-19 and the demand on healthcare systems are the compounded result of long-standing structural inequalities in US society. Strategies to protect those in the most vulnerable counties will require urgent measures to better support communities’ attempts at social distancing and to accelerate cooperation across jurisdictions to supply personnel and equipment to counties that will experience high demand.Publication Using predicted imports of 2019-nCoV cases to determine locations that may not be identifying all imported cases(2020-02-05) Martinez de Salazar Munoz, Pablo; Niehus, Rene; Taylor, Aimee; Buckee, Caroline; Lipsitch, MarcCases from the ongoing outbreak of atypical pneumonia caused by the 2019 novel coronavirus (2019-nCoV) exported from mainland China can lead to self-sustained outbreaks in other populations. Internationally imported cases are currently being reported in several different locations. Early detection of imported cases is critical for containment of the virus. Based on air travel volume estimates from Wuhan to international destinations and using a generalized linear regression model we identify locations which may potentially have undetected internationally imported cases.Publication Every Body Counts: Measuring Mortality From the COVID-19 Pandemic(American College of Physicians, 2020-09-11) Kiang, Mathew; Irizarry, Rafael; Buckee, Caroline; Balsari, SatchitAs of mid-August 2020, more than 170 000 U.S. residents have died of coronavirus disease 2019 (COVID-19); however, the true number of deaths resulting from COVID-19, both directly and indirectly, is likely to be much higher. The proper attribution of deaths to this pandemic has a range of societal, legal, mortuary, and public health consequences. This article discusses the current difficulties of disaster death attribution and describes the strengths and limitations of relying on death counts from death certificates, estimations of indirect deaths, and estimations of excess mortality. Improving the tabulation of direct and indirect deaths on death certificates will require concerted efforts and consensus across medical institutions and public health agencies. In addition, actionable estimates of excess mortality will require timely access to standardized and structured vital registry data, which should be shared directly at the state level to ensure rapid response for local governments. Correct attribution of direct and indirect deaths and estimation of excess mortality are complementary goals that are critical to our understanding of the pandemic and its effect on human life.Publication Socioeconomic Status Determines COVID-19 Incidence and Related Mortality in Santiago, Chile(2021-01-15) Mena, Gonzalo E.; Martinez Vargas, Pamela; Mahmud, Ayesha; Marquet, Pablo A.; Buckee, Caroline; Santillana, MauricioThe current coronavirus disease 2019 (COVID-19) pandemic has impacted dense urban populations particularly hard. Here, we provide an in-depth characterization of disease incidence and mortality patterns, and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. We find that among all age groups, there is a strong association between socioeconomic status and both mortality –measured either by direct COVID-19 attributed deaths or excess deaths– and public health capacity. Specifically, we show that behavioral factors like human mobility, as well as health system factors such as testing volumes, testing delays, and test positivity rates are associated with disease outcomes. These robust patterns suggest multiple possibly interacting pathways that can explain the observed disease burden and mortality differentials: (i) in lower socioeconomic status municipalities, human mobility was not reduced as much as in more affluent municipalities; (ii) testing volumes in these locations were insufficient early in the pandemic and public health interventions were applied too late to be effective; (iii) test positivity and testing delays were much higher in less affluent municipalities, indicating an impaired capacity of the health-care system to contain the spread of the epidemic; and (iv) infection fatality rates appear much higher in the lower end of the socioeconomic spectrum. Together, these findings highlight the exacerbated consequences of health-care inequalities in a large city of the developing world, and provide practical methodological approaches useful for characterizing COVID-19 burden and mortality in other segregated urban centers.Publication Reductions in commuting mobility predict geographic differences in SARS-CoV-2 prevalence in New York City(2020) Kissler, Stephen; Kishore, Nishant; Prabhu, Malavika; Goffman, Dena; Beilin, Yaakov; Landau, Ruth; Gyamfi-Bannerman, Cynthia; Bateman, Brian; Katz, Daniel; Gal, Jonathan; Bianco, Angela; Stone, Joanne; Larremore, Daniel; Buckee, Caroline; Grad, YonatanImportance: New York City is the epicenter of the SARS-CoV-2 pandemic in the United States. Mortality and hospitalizations have differed substantially between different neighborhoods. Mitigation efforts in the coming months will require knowing the extent of geographic variation in SARS-CoV-2 prevalence and understanding the drivers of these differences. Objective: To estimate the prevalence of SARS-CoV-2 infection by New York City borough between March 22nd and May 3rd, 2020, and to associate variation in prevalence with antecedent reductions in mobility, defined as aggregated daily physical movements into and out of each borough. Design: Observational study of universal SARS-CoV-2 test results obtained from women hospitalized for delivery. Setting: Four New York-Presbyterian hospital campuses and two Mount Sinai hospital campuses in New York City. Participants: 1,746 women with New York City ZIP codes hospitalized for delivery. Exposures: Infection with SARS-CoV-2. Main outcomes: Population prevalence of SARS-CoV-2 by borough and correlation with the reduction in daily commuting-style movements into and out of each borough. Results: The estimated population prevalence of SARS-CoV-2 ranged from 11.3% (95% credible interval 8.9%, 13.9%) in Manhattan to 26.0% (95% credible interval 15.3%, 38.9%) in South Queens, with an estimated city-wide prevalence of 15.6% (95% credible interval 13.9%, 17.4%). The peak city-wide prevalence was during the week of March 30th, though temporal trends in prevalence varied substantially between boroughs. Population revalence was lowest in boroughs with the greatest reductions in morning commutes out of and evening commutes into the borough (Pearson R = –0.88, 95% credible interval –0.52, –0.99). Conclusions and relevance: Reductions in between-borough mobility predict geographic differences in the prevalence of SARS-CoV-2 infection in New York City. Large parts of the city may remain at risk for substantial SARS-CoV-2 outbreaks. Widespread testing should be conducted to identify geographic disparities in prevalence and assess the risk of future outbreaks.Publication Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics(Nature Publishing Group UK, 2017) Wesolowski, Amy; zu Erbach-Schoenberg, Elisabeth; Tatem, Andrew J.; Lourenço, Christopher; Viboud, Cecile; Charu, Vivek; Eagle, Nathan; Engø-Monsen, Kenth; Qureshi, Taimur; Buckee, Caroline; Metcalf, C. J. E.Seasonal variation in human mobility is globally ubiquitous and affects the spatial spread of infectious diseases, but the ability to measure seasonality in human movement has been limited by data availability. Here, we use mobile phone data to quantify seasonal travel and directional asymmetries in Kenya, Namibia, and Pakistan, across a spectrum from rural nomadic populations to highly urbanized communities. We then model how the geographic spread of several acute pathogens with varying life histories could depend on country-wide connectivity fluctuations through the year. In all three countries, major national holidays are associated with shifts in the scope of travel. Within this broader pattern, the relative importance of particular routes also fluctuates over the course of the year, with increased travel from rural to urban communities after national holidays, for example. These changes in travel impact how fast communities are likely to be reached by an introduced pathogen.Publication Mortality in Puerto Rico after Hurricane Maria(New England Journal of Medicine (NEJM/MMS), 2018) Kishore, Nishant; Marqués, Domingo; Mahmud, Ayesha; Kiang, Mathew; Rodriguez, Irmary; Fuller, Arlan; Ebner, Peggy; Sorensen, Cecilia; Racy, Fabio De Castro Jorge; Lemery, Jay; Maas, Leslie; Leaning, Jennifer; Irizarry, Rafael; Balsari, Satchit; Buckee, CarolineBACKGROUND Quantifying the effect of natural disasters on society is critical for recovery of public health services and infrastructure. The death toll can be difficult to assess in the aftermath of a major disaster. In September 2017, Hurricane Maria caused massive infrastructural damage to Puerto Rico, but its effect on mortality remains contentious. The official death count is 64. METHODS Using a representative, stratified sample, we surveyed 3299 randomly chosen households across Puerto Rico to produce an independent estimate of all-cause mortality after the hurricane. Respondents were asked about displacement, infrastructure loss, and causes of death. We calculated excess deaths by comparing our estimated post-hurricane mortality rate with official rates for the same period in 2016. RESULTS From the survey data, we estimated a mortality rate of 14.3 deaths (95% confidence interval [CI], 9.8 to 18.9) per 1000 persons from September 20 through December 31, 2017. This rate yielded a total of 4645 excess deaths during this period (95% CI, 793 to 8498), equivalent to a 62% increase in the mortality rate as compared with the same period in 2016. However, this number is likely to be an underestimate because of survivor bias. The mortality rate remained high through the end of December 2017, and one third of the deaths were attributed to delayed or interrupted health care. Hurricane-related migration was substantial. CONCLUSIONS This household-based survey suggests that the number of excess deaths related to Hurricane Maria in Puerto Rico is more than 70 times the official estimate. (Funded by the Harvard T.H. Chan School of Public Health and others.)Publication Productive disruption: opportunities and challenges for innovation in infectious disease surveillance(BMJ Publishing Group, 2018) Buckee, Caroline; Cardenas, Maria I E; Corpuz, June; Ghosh, Arpita; Haque, Farhana; Karim, Jahirul; Mahmud, Ayesha; Maude, Richard; Mensah, Keitly; Motaze, Nkengafac Villyen; Nabaggala, Maria; Metcalf, Charlotte Jessica Eland; Mioramalala, Sedera Aurélien; Mubiru, Frank; Peak, Corey M.; Pramanik, Santanu; Rakotondramanga, Jean Marius; Remera, Eric; Sinha, Ipsita; Sovannaroth, Siv; Tatem, Andrew J; Zaw, WinPublication Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead(The Royal Society, 2017) Metcalf, C. Jessica E.; Walter, Katharine S.; Wesolowski, Amy; Buckee, Caroline; Shevliakova, Elena; Tatem, Andrew J.; Boos, William R.; Weinberger, Daniel M.; Pitzer, Virginia E.Climate change is likely to profoundly modulate the burden of infectious diseases. However, attributing health impacts to a changing climate requires being able to associate changes in infectious disease incidence with the potentially complex influences of climate. This aim is further complicated by nonlinear feedbacks inherent in the dynamics of many infections, driven by the processes of immunity and transmission. Here, we detail the mechanisms by which climate drivers can shape infectious disease incidence, from direct effects on vector life history to indirect effects on human susceptibility, and detail the scope of variation available with which to probe these mechanisms. We review approaches used to evaluate and quantify associations between climate and infectious disease incidence, discuss the array of data available to tackle this question, and detail remaining challenges in understanding the implications of climate change for infectious disease incidence. We point to areas where synthesis between approaches used in climate science and infectious disease biology provide potential for progress.