Planning For The Next Pandemic: Lab Systems Need Policy Shift To Speed Emerging Infectious Disease Warning And Tracking.

Early detection and ongoing monitoring of infectious diseases depends on diagnostic testing. The US has a large, diverse system of public, academic, and private laboratories that develop new diagnostic tests; perform routine testing; and conduct specialized reference testing, such as genomic sequencing. These laboratories operate under a complex mix of laws and regulations at the federal, state, and local levels. The COVID-19 pandemic exposed major weaknesses in the nation's laboratory system, some of which were seen again during the global mpox outbreak in 2022. In this article we review how the US laboratory system has been designed to detect and monitor emerging infections, describe what gaps were revealed during COVID-19, and propose specific steps that policy makers can take both to strengthen the current system and to prepare the US for the next pandemic.

Advancing detection and response capacities for emerging and re-emerging pathogens in Africa.

Recurrent disease outbreaks caused by a range of emerging and resurging pathogens over the past decade reveal major gaps in public health preparedness, detection, and response systems in Africa. Underlying causes of recurrent disease outbreaks include inadequacies in the detection of new infectious disease outbreaks in the community, in rapid pathogen identification, and in proactive surveillance systems. In sub-Saharan Africa, where 70% of zoonotic outbreaks occur, there remains the perennial risk of outbreaks of new or re-emerging pathogens for which no vaccines or treatments are available. As the Ebola virus disease, COVID-19, and mpox (formerly known as monkeypox) outbreaks highlight, a major paradigm shift is required to establish an effective infrastructure and common frameworks for preparedness and to prompt national and regional public health responses to mitigate the effects of future pandemics in Africa.

Contact tracing reveals community transmission of COVID-19 in New York City.

Understanding SARS-CoV-2 transmission within and among communities is critical for tailoring public health policies to local context. However, analysis of community transmission is challenging due to a lack of high-resolution surveillance and testing data. Here, using contact tracing records for 644,029 cases and their contacts in New York City during the second pandemic wave, we provide a detailed characterization of the operational performance of contact tracing and reconstruct exposure and transmission networks at individual and ZIP code scales. We find considerable heterogeneity in reported close contacts and secondary infections and evidence of extensive transmission across ZIP code areas. Our analysis reveals the spatial pattern of SARS-CoV-2 spread and communities that are tightly interconnected by exposure and transmission. We find that locations with higher vaccination coverage and lower numbers of visitors to points-of-interest had reduced within- and cross-ZIP code transmission events, highlighting potential measures for curtailing SARS-CoV-2 spread in urban settings.

The effect of a proof-of-vaccination requirement, incentive payments, and employer-based mandates on COVID-19 vaccination rates in New York City: a synthetic-control analysis.

BACKGROUND: COVID-19 vaccines have been available to all adults in the USA since April, 2021, but many adults remain unvaccinated. We aimed to assess the joint effect of a proof-of-vaccination requirement, incentive payments, and employer-based mandates on rates of adult vaccination in New York City (NYC). METHODS: We constructed a synthetic control group for NYC composed of other counties in the core of large, metropolitan areas in the USA. The vaccination outcomes for NYC were compared against those of the synthetic control group from July 26, 2021, to Nov 1, 2021, to determine the differential effects of the policies. Analyses were conducted on county-level vaccination data reported by the Centers for Disease Control and Prevention. The synthetic control group was constructed by matching on county-level preintervention vaccination outcomes, partisanship, economic attributes, demographics, and metropolitan area population. Statistical inference was conducted using placebo tests for non-treated counties. FINDINGS: The synthetic control group resembled NYC across attributes used in the matching process. The cumulative adult vaccination rate for NYC (in adults aged 18 years or older who received at least one dose of an authorised COVID-19 vaccine) increased from 72·5% to 89·4% (+16·9 percentage points [pp]) during the intervention period, compared with an increase from 72·5% to 83·2% (+10·7 pp) for the synthetic control group, a difference of 6·2 pp (95% CI 1·4-10·7), or 410 201 people (90 966-706 532). Daily vaccinations for NYC were consistently higher than those in the synthetic control group, a pattern that started shortly after the start of the intervention period. INTERPRETATION: The combination of a proof-of-vaccination requirement, incentive payments, and vaccine mandates increased vaccination rates among adults in NYC compared with jurisdictions that did not use the same measures. Whether the impact of these measures occurred by inducing more people to get vaccinated, or by accelerating vaccinations that would have occurred later, the increase in vaccination rates likely averted illness and death. FUNDING: None.