Historical trends demonstrate a pattern of increasingly frequent and severe spillover events of high-consequence zoonotic viruses.

The COVID-19 pandemic has focused attention on patterns of infectious disease spillover. Climate and land-use changes are predicted to increase the frequency of zoonotic spillover events, which have been the cause of most modern epidemics. Characterising historical trends in zoonotic spillover can provide insights into the expected frequency and severity of future epidemics, but historical epidemiological data remains largely fragmented and difficult to analyse. We utilised our extensive epidemiological database to analyse a specific subset of high-consequence zoonotic spillover events for trends in the annual frequency and severity of outbreaks. Our analysis, which excludes the ongoing SARS-CoV-2 pandemic, shows that the number of spillover events and reported deaths have been increasing by 4.98% (confidence interval [CI]95% [3.22%; 6.76%]) and 8.7% (CI 95% [4.06%; 13.62%]) annually, respectively. This trend can be altered by concerted global efforts to improve our capacity to prevent and contain outbreaks. Such efforts are needed to address this large and growing risk to global health.

Infectious Disease Underreporting Is Predicted by Country-Level Preparedness, Politics, and Pathogen Severity.

Underreporting of infectious diseases is a pervasive challenge in public health that has emerged as a central issue in characterizing the dynamics of the COVID-19 pandemic. Infectious diseases are underreported for a range of reasons, including mild or asymptomatic infections, weak public health infrastructure, and government censorship. In this study, we investigated factors associated with cross-country and cross-pathogen variation in reporting. We performed a literature search to collect estimates of empirical reporting rates, calculated as the number of cases reported divided by the estimated number of true cases. This literature search yielded a dataset of reporting rates for 32 pathogens, representing 52 countries. We combined epidemiological and social science theory to identify factors specific to pathogens, country health systems, and politics that could influence empirical reporting rates. We performed generalized linear regression to test the relationship between the pathogen- and country-specific factors that we hypothesized could influence reporting rates, and the reporting rate estimates that we collected in our literature search. Pathogen- and country-specific factors were predictive of reporting rates. Deadlier pathogens and sexually transmitted diseases were more likely to be reported. Country epidemic preparedness was positively associated with reporting completeness, while countries with high levels of media bias in favor of incumbent governments were less likely to report infectious disease cases. Underreporting is a complex phenomenon that is driven by factors specific to pathogens, country health systems, and politics. In this study, we identified specific and measurable components of these broader factors that influence pathogen- and country-specific reporting rates and used model selection techniques to build a model that can guide efforts to diagnose, characterize, and reduce underreporting. Furthermore, this model can characterize uncertainty and correct for bias in reported infectious disease statistics, particularly when outbreak-specific empirical estimates of underreporting are unavailable. More precise estimates can inform control policies and improve the accuracy of infectious disease models.

Challenges in reported COVID-19 data: best practices and recommendations for future epidemics.

The proliferation of composite data sources tracking the COVID-19 pandemic emphasises the need for such databases during large-scale infectious disease events as well as the potential pitfalls due to the challenges of combining disparate data sources. Multiple organisations have attempted to standardise the compilation of disparate data from multiple sources during the COVID-19 pandemic. However, each composite data source can use a different approach to compile data and address data issues with varying results.We discuss some best practices for researchers endeavouring to create such compilations while discussing three key categories of challenges: (1) data dissemination, which includes discrepant estimates and varying data structures due to multiple agencies and reporting sources generating public health statistics on the same event; (2) data elements, such as date formats and location names, lack standardisation, and differing spatial and temporal resolutions often create challenges when combining sources; and (3) epidemiological factors, including missing data, reporting lags, retrospective data corrections and changes to case definitions that cannot easily be addressed by the data compiler but must be kept in mind when reviewing the data.Efforts to reform the global health data ecosystem should bear such challenges in mind. Standards and best practices should be developed and incorporated to yield more robust, transparent and interoperable data. Since no standards exist yet, we have highlighted key challenges in creating a comprehensive spatiotemporal view of outbreaks from multiple, often discrepant, reporting sources and provided guidelines to address them. In general, we caution against an over-reliance on fully automated systems for integrating surveillance data and strongly advise that epidemiological experts remain engaged in the process of data assessment, integration, validation and interpretation to identify, diagnose and resolve data challenges.

Debunking highly prevalent health misinformation using audio dramas delivered by WhatsApp: evidence from a randomised controlled trial in Sierra Leone.

INTRODUCTION: Infectious disease misinformation is widespread and poses challenges to disease control. There is limited evidence on how to effectively counter health misinformation in a community setting, particularly in low-income regions, and unsettled scientific debate about whether misinformation should be directly discussed and debunked, or implicitly countered by providing scientifically correct information. METHODS: The Contagious Misinformation Trial developed and tested interventions designed to counter highly prevalent infectious disease misinformation in Sierra Leone, namely the beliefs that (1) mosquitoes cause typhoid and (2) typhoid co-occurs with malaria. The information intervention for group A (n=246) explicitly discussed misinformation and explained why it was incorrect and then provided the scientifically correct information. The intervention for group B (n=245) only focused on providing correct information, without directly discussing related misinformation. Both interventions were delivered via audio dramas on WhatsApp that incorporated local cultural understandings of typhoid. Participants were randomised 1:1:1 to the intervention groups or the control group (n=245), who received two episodes about breast feeding. RESULTS: At baseline 51% believed that typhoid is caused by mosquitoes and 59% believed that typhoid and malaria always co-occur. The endline survey was completed by 91% of participants. Results from the intention-to-treat, per-protocol and as-treated analyses show that both interventions substantially reduced belief in misinformation compared with the control group. Estimates from these analyses, as well as an exploratory dose-response analysis, suggest that direct debunking may be more effective at countering misinformation. Both interventions improved people's knowledge and self-reported behaviour around typhoid risk reduction, and yielded self-reported increases in an important preventive method, drinking treated water. CONCLUSION: These results from a field experiment in a community setting show that highly prevalent health misinformation can be countered, and that direct, detailed debunking may be most effective. TRIAL REGISTRATION NUMBER: NCT04112680.

Measuring development assistance for health systems strengthening and health security: an analysis using the Creditor Reporting System database.

Background: Health systems strengthening (HSS) and health security are two pillars of universal health coverage (UHC). Investments in these areas are essential for meeting the Sustainable Development Goals and are of heightened relevance given the emergence of the 2019 novel coronavirus disease (COVID-19). This study aims to generate information on development assistance for health (DAH) for these areas, including how to track it and how funding levels align with country needs. Methods: We developed a framework to analyze the amount of DAH disbursed in 2015 for the six building blocks of the health system ('system-wide HSS') plus health security (emergency preparedness, risk management, and response) at both the global (transnational) and country level. We reviewed 2,427 of 32,801 DAH activities in the Creditor Reporting System (CRS) database (80% of the total value of disbursements in 2015) and additional public information sources. Additional aid activities were identified through a keyword search. Results: In 2015, we estimated that US$3.1 billion (13.4%) of the US$22.9 billion of DAH captured in the CRS database was for system-wide HSS and health security: US$2.5 billion (10.9%) for system-wide HSS, mostly for infrastructure, and US$0.6 billion (2.5%) for system-wide health security. US$567.1 million (2.4%) was invested in supporting these activities at the global level. If responses to individual health emergencies are included, 7.5% of total DAH (US$1.7B) was for health security. We found a correlation between DAH for HSS and maternal mortality rates, and we interpret this as evidence that HSS aid generally flowed to countries with greater need. Conclusions : Achieving UHC by 2030 will require greater investments in system-wide HSS and proactive health emergency preparedness. It may be appropriate for donors to more prominently consider country needs and global functions when investing in health security and HSS.

Using social media to estimate Zika's impact on tourism: #babymoon, 2014-2017.

Zika virus infection during pregnancy can cause microcephaly and other birth defects. We hypothesized that the Latin America Zika epidemic resulted in pregnant women and their partners adopting behavioral changes to limit risk, leading them to forego travel to Zika-affected locations. We evaluated this hypothesis by studying travelers' intent and behavior through Twitter data related to babymoon: a holiday taken by parents-to-be before their baby is born. We found the odds of mentioning representative Zika-affected locations in #babymoon tweets dropped significantly (Odds ratio: 0.29, 95% CI: 0.20-0.40) after the Zika-microcephaly association became well-known. This result was further corroborated through a content analysis of #babymoon tweets mentioning Zika-affected locations, which identified if the Twitter user was physically present in the Zika-affected locations. Conversely, we found a small but statistically insignificant increase in the odds of mentioning Zika-free locations from #babymoon tweets (Odds Ratio: 1.11, 95% CI: 0.97-1.27) after the Zika-microcephaly association became well-known.

Assessing global preparedness for the next pandemic: development and application of an Epidemic Preparedness Index.

INTRODUCTION: Robust metrics for national-level preparedness are critical for assessing global resilience to epidemic and pandemic outbreaks. However, existing preparedness assessments focus primarily on public health systems or specific legislative frameworks, and do not measure other essential capacities that enable and support public health preparedness and response. METHODS: We developed an Epidemic Preparedness Index (EPI) to assess national-level preparedness. The EPI is global, covering 188 countries. It consists of five subindices measuring each country's economic resources, public health communications, infrastructure, public health systems and institutional capacity. To evaluate the construct validity of the EPI, we tested its correlation with proxy measures for preparedness and response capacity, including the timeliness of outbreak detection and reporting, as well as vaccination rates during the 2009 H1N1 influenza pandemic. RESULTS: The most prepared countries were concentrated in Europe and North America, while the least prepared countries clustered in Central and West Africa and Southeast Asia. Better prepared countries were found to report infectious disease outbreaks more quickly and to have vaccinated a larger proportion of their population during the 2009 pandemic. CONCLUSION: The EPI measures a country's capacity to detect and respond to infectious disease events. Existing tools, such as the Joint External Evaluation (JEE), have been designed to measure preparedness within a country over time. The EPI complements the JEE by providing a holistic view of preparedness and is constructed to support comparative risk assessment between countries. The index can be updated rapidly to generate global estimates of pandemic preparedness that can inform strategy and resource allocation.