Emerging dynamics from high-resolution spatial numerical epidemics.

Simulating nationwide realistic individual movements with a detailed geographical structure can help optimise public health policies. However, existing tools have limited resolution or can only account for a limited number of agents. We introduce Epidemap, a new framework that can capture the daily movement of more than 60 million people in a country at a building-level resolution in a realistic and computationally efficient way. By applying it to the case of an infectious disease spreading in France, we uncover hitherto neglected effects, such as the emergence of two distinct peaks in the daily number of cases or the importance of local density in the timing of arrival of the epidemic. Finally, we show that the importance of super-spreading events strongly varies over time.

Comparing Methods for Record Linkage for Public Health Action: Matching Algorithm Validation Study.

BACKGROUND: Many public health departments use record linkage between surveillance data and external data sources to inform public health interventions. However, little guidance is available to inform these activities, and many health departments rely on deterministic algorithms that may miss many true matches. In the context of public health action, these missed matches lead to missed opportunities to deliver interventions and may exacerbate existing health inequities. OBJECTIVE: This study aimed to compare the performance of record linkage algorithms commonly used in public health practice. METHODS: We compared five deterministic (exact, Stenger, Ocampo 1, Ocampo 2, and Bosh) and two probabilistic record linkage algorithms (fastLink and beta record linkage [BRL]) using simulations and a real-world scenario. We simulated pairs of datasets with varying numbers of errors per record and the number of matching records between the two datasets (ie, overlap). We matched the datasets using each algorithm and calculated their recall (ie, sensitivity, the proportion of true matches identified by the algorithm) and precision (ie, positive predictive value, the proportion of matches identified by the algorithm that were true matches). We estimated the average computation time by performing a match with each algorithm 20 times while varying the size of the datasets being matched. In a real-world scenario, HIV and sexually transmitted disease surveillance data from King County, Washington, were matched to identify people living with HIV who had a syphilis diagnosis in 2017. We calculated the recall and precision of each algorithm compared with a composite standard based on the agreement in matching decisions across all the algorithms and manual review. RESULTS: In simulations, BRL and fastLink maintained a high recall at nearly all data quality levels, while being comparable with deterministic algorithms in terms of precision. Deterministic algorithms typically failed to identify matches in scenarios with low data quality. All the deterministic algorithms had a shorter average computation time than the probabilistic algorithms. BRL had the slowest overall computation time (14 min when both datasets contained 2000 records). In the real-world scenario, BRL had the lowest trade-off between recall (309/309, 100.0%) and precision (309/312, 99.0%). CONCLUSIONS: Probabilistic record linkage algorithms maximize the number of true matches identified, reducing gaps in the coverage of interventions and maximizing the reach of public health action.

Finding the Edges of Problems: Social Media as an Exploratory Research Tool.

Social media use in public health and other health related research applications has seen a rapid increase in recent years. However, there has been very limited utilization of this growing digital sector in agricultural injury research. Social media offers immense potential in gathering informal data, both text and images, converting them into knowledge, which can open up avenues for research, policy, and practice. There are a number of ways social media data can be utilized in agricultural injury research. This paper touches on the adoption of these data sources in health research and discusses the use of social media as an exploratory research tool that can peer into and identify the edges of potential health and safety problems.

Societal Effects Are a Major Factor for the Uptake of the Coronavirus Disease 2019 (COVID-19) Digital Contact Tracing App in The Netherlands.

OBJECTIVES: Our study investigates the extent to which uptake of a COVID-19 digital contact-tracing (DCT) app among the Dutch population is affected by its configurations, its societal effects, and government policies toward such an app. METHODS: We performed a discrete choice experiment among Dutch adults including 7 attributes, that is, who gets a notification, waiting time for testing, possibility for shops to refuse customers who have not installed the app, stopping condition for contact tracing, number of people unjustifiably quarantined, number of deaths prevented, and number of households with financial problems prevented. The data were analyzed by means of panel mixed logit models. RESULTS: The prevention of deaths and financial problems of households had a very strong influence on the uptake of the app. Predicted app uptake rates ranged from 24% to 78% for the worst and best possible app for these societal effects. We found a strong positive relationship between people's trust in government and people's propensity to install the DCT app. CONCLUSIONS: The uptake levels we find are much more volatile than the uptake levels predicted in comparable studies that did not include societal effects in their discrete choice experiments. Our finding that the societal effects are a major factor in the uptake of the DCT app results in a chicken-or-the-egg causality dilemma. That is, the societal effects of the app are severely influenced by the uptake of the app, but the uptake of the app is severely influenced by its societal effects.

Modeling Social Distancing Strategies to Prevent SARS-CoV-2 Spread in Israel: A Cost-Effectiveness Analysis.

OBJECTIVES: While highly effective in preventing SARS-CoV-2 spread, national lockdowns come with an enormous economic price. Few countries have adopted an alternative "testing, tracing, and isolation" approach to selectively isolate people at high exposure risk, thereby minimizing the economic impact. To assist policy makers, we performed a cost-effectiveness analysis of these 2 strategies. METHODS: A modified Susceptible, Exposed, Infectious, Recovered, and Deceased (SEIRD) model was employed to assess the situation in Israel, a small country with ∼9 million people. The incremental cost-effectiveness ratio (ICER) of these strategies as well as the expected number of infected individuals and deaths were calculated. RESULTS: A nationwide lockdown is expected to save, on average, 274 (median 124, interquartile range: 71-221) lives compared to the "testing, tracing, and isolation" approach. However, the ICER will be, on average, $45 104 156 (median $49.6 million, interquartile range: 22.7-220.1) to prevent 1 case of death. CONCLUSION: A national lockdown has a moderate advantage in saving lives with tremendous costs and possible overwhelming economic effects. These findings should assist decision makers dealing with additional waves of this pandemic.

The ethics of refusing to care for patients during the coronavirus pandemic: A Chinese perspective.

As a result of the coronavirus (COVID-19) pandemic, health professionals are faced with situations they have not previously encountered and are being forced to make difficult ethical decisions. As the first group to experience challenges of caring for patients with coronavirus, Chinese nurses endure heartbreak and face stressful moral dilemmas. In this opinion piece, we examine three related critical questions: Whether society has the right to require health professionals to risk their lives caring for patients; whether health professionals have the right to refuse to care for patients during the coronavirus pandemic; and what obligations there are to protect health professionals? Value of care, community expectations, legal obligations, professional and codes of practice may compel health professionals to put themselves at risks in emergency situations. The bioethical principles of autonomy, justice, beneficence and non-maleficence, as well as public health ethics, guide nurses to justify their decisions as to whether they are entitled to refuse to treat COVID-19 patients during the pandemic. We hope that the open discussion would support the international society in addressing similar ethical challenges in their respective situations during this public health crisis.

Mitigating the Twin Threats of Climate-Driven Atlantic Hurricanes and COVID-19 Transmission.

The co-occurrence of the 2020 Atlantic hurricane season and the ongoing coronavirus disease 2019 (COVID-19) pandemic creates complex dilemmas for protecting populations from these intersecting threats. Climate change is likely contributing to stronger, wetter, slower-moving, and more dangerous hurricanes. Climate-driven hazards underscore the imperative for timely warning, evacuation, and sheltering of storm-threatened populations - proven life-saving protective measures that gather evacuees together inside durable, enclosed spaces when a hurricane approaches. Meanwhile, the rapid acquisition of scientific knowledge regarding how COVID-19 spreads has guided mass anti-contagion strategies, including lockdowns, sheltering at home, physical distancing, donning personal protective equipment, conscientious handwashing, and hygiene practices. These life-saving strategies, credited with preventing millions of COVID-19 cases, separate and move people apart. Enforcement coupled with fear of contracting COVID-19 have motivated high levels of adherence to these stringent regulations. How will populations react when warned to shelter from an oncoming Atlantic hurricane while COVID-19 is actively circulating in the community? Emergency managers, health care providers, and public health preparedness professionals must create viable solutions to confront these potential scenarios: elevated rates of hurricane-related injury and mortality among persons who refuse to evacuate due to fear of COVID-19, and the resurgence of COVID-19 cases among hurricane evacuees who shelter together.

Shelter Hospital: Glimmers of Hope in Treating Coronavirus 2019.

Since the first report of the 2019 novel coronavirus disease (COVID-19) in December 2019 in Wuhan, China, the outbreak of the disease has been continuously evolving. Until March 17, 2020, 185, 178 cases had been confirmed, including 81,134 cases in China and 104,044 cases outside of China. In this comment, we report the unexpected beneficial effect of a deployable rapid-assembly shelter hospital on the prevention and treatment of COVID-19. We describe the shelter hospital maintenance, treatment mode and primary treatment methods, which will provide a valuable experience in dealing with public health emergencies, such as COVID-19, for other countries and areas.