Most pandemics-eg, HIV/AIDS, severe acute respiratory syndrome, pandemic influenzaoriginate in animals, are caused by viruses, and are driven to emerge by ecological, behavioural, or socioeconomic changes. Despite their substantial effects on global public health and growing understanding of the process by which they emerge, no pandemic has been predicted before infecting human beings. We review what is known about the pathogens that emerge, the hosts that they originate in, and the factors that drive their emergence. We discuss challenges to their control and new efforts to predict pandemics, target surveillance to the most crucial interfaces, and identify prevention strategies. New mathematical modelling, diagnostic, communications, and informatics technologies can identify and report hitherto unknown microbes in other species, and thus new risk assessment approaches are needed to identify microbes most likely to cause human disease. We lay out a series of research and surveillance opportunities and goals that could help to overcome these challenges and move the global pandemic strategy from response to pre-emption.

Zoonoses originating from wildlife represent a significant threat to global health, security and economic growth, and combatting their emergence is a public health priority. However, our understanding of the mechanisms underlying their emergence remains rudimentary. Here we update a global database of emerging infectious disease (EID) events, create a novel measure of reporting effort, and fit boosted regression tree models to analyze the demographic, environmental and biological correlates of their occurrence. After accounting for reporting effort, we show that zoonotic EID risk is elevated in forested tropical regions experiencing land-use changes and where wildlife biodiversity (mammal species richness) is high. We present a new global hotspot map of spatial variation in our zoonotic EID risk index, and partial dependence plots illustrating relationships between events and predictors. Our results may help to improve surveillance and long-term EID monitoring programs, and design field experiments to test underlying mechanisms of zoonotic disease emergence.

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Catastrophic disasters create surge capacity needs for health care systems. This is especially true in the urban setting because the high population density and reliance on complex urban infrastructures (e.g., mass transit systems and high rise buildings) could adversely affect the ability to meet surge capacity needs. To better understand responsiveness in this setting, we conducted a survey of health care workers (HCWs) (N =6,428) from 47 health care facilities in New York City and the surrounding metropolitan region to determine their ability and willingness to report to work during various catastrophic events. A range of facility types and sizes were represented in the sample. Results indicate that HCWs were most able to report to work for a mass casualty incident (MCI) (83%), environmental disaster (81%), and chemical event (71%) and least able to report during a smallpox epidemic (69%), radiological event (64%), sudden acute respiratory distress syndrome (SARS) outbreak (64%), or severe snow storm (49%). In terms of willingness, HCWs were most willing to report during a snow storm (80%), MCI (86%), and environmental disaster (84%) and least willing during a SARS outbreak (48%), radiological event (57%), smallpox epidemic (61%), and chemical event (68%). Barriers to ability included transportation problems, child care, eldercare, and pet care obligations. Barriers to willingness included fear and concern for family and self and personal health problems. The findings were consistent for all types of facilities. Importantly, many of the barriers identified are amenable to interventions.

Since the emergence of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and Middle East Respiratory Syndrom Coronavirus (MERS-CoV) it has become increasingly clear that bats are important reservoirs of CoVs. Despite this, only 6% of all CoV sequences in GenBank are from bats. The remaining 94% largely consist of known pathogens of public health or agricultural significance, indicating that current research effort is heavily biased towards describing known diseases rather than the ‘pre-emergent’ diversity in bats. Our study addresses this critical gap, and focuses on resource poor countries where the risk of zoonotic emergence is believed to be highest. We surveyed the diversity of CoVs in multiple host taxa from twenty countries to explore the factors driving viral diversity at a global scale. We identified sequences representing 100 discrete phylogenetic clusters, ninety-one of which were found in bats, and used ecological and epidemiologic analyses to show that patterns of CoV diversity correlate with those of bat diversity. This cements bats as the major evolutionary reservoirs and ecological drivers of CoV diversity. Co-phylogenetic reconciliation analysis was also used to show that host switching has contributed to CoV evolution, and a preliminary analysis suggests that regional variation exists in the dynamics of this process. Overall our study represents a model for exploring global viral diversity and advances our fundamental understanding of CoV biodiversity and the potential risk factors associated with zoonotic emergence.

The 1918 ''Spanish flu'' was the fastest spreading and most deadly influenza pandemic in recorded history. Hypotheses of its origin have been based on a limited collection of case and outbreak reports from before its recognized European emergence in the summer of 1918. These anecdotal accounts, however, remain insufficient for determining the early diffusion and impact of the pandemic virus. Using routinely collected monthly age-stratified mortality data, we show that an unmistakable shift in the age distribution of epidemic deaths occurred during the 1917͞1918 influenza season in New York City. The timing, magnitude, and age distribution of this mortality shift provide strong evidence that an early wave of the pandemic virus was present in New York City during February-April 1918.age-specific mortality ͉ epidemic ͉ herald wave ͉ Spanish flu

The majority of emerging zoonoses originate in wildlife, and many are caused by viruses. However, there are no rigorous estimates of total viral diversity (here termed “virodiversity”) for any wildlife species, despite the utility of this to future surveillance and control of emerging zoonoses. In this case study, we repeatedly sampled a mammalian wildlife host known to harbor emerging zoonotic pathogens (the Indian Flying Fox, Pteropus giganteus) and used PCR with degenerate viral family-level primers to discover and analyze the occurrence patterns of 55 viruses from nine viral families. We then adapted statistical techniques used to estimate biodiversity in vertebrates and plants and estimated the total viral richness of these nine families in P. giganteus to be 58 viruses. Our analyses demonstrate proof-of-concept of a strategy for estimating viral richness and provide the first statistically supported estimate of the number of undiscovered viruses in a mammalian host. We used a simple extrapolation to estimate that there are a minimum of 320,000 mammalian viruses awaiting discovery within these nine families, assuming all species harbor a similar number of viruses, with minimal turnover between host species. We estimate the cost of discovering these viruses to be ~$6.3 billion (or ~$1.4 billion for 85% of the total diversity), which if annualized over a 10-year study time frame would represent a small fraction of the cost of many pandemic zoonoses.

In this population, there was no detectable additional benefit of hand sanitizer or face masks over targeted education on overall rates of URIs, but mask wearing was associated with reduced secondary transmission and should be encouraged during outbreak situations. During the study period, community concern about methicillin-resistant Staphylococcus aureus was occurring, perhaps contributing to the use of hand sanitizer in the Education control group, and diluting the intervention's measurable impact.