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Rationale: Most U.S. residents who develop tuberculosis (TB) were born abroad, and U.S. TB incidence is increasingly driven by infection risks in other countries. Objectives: To estimate the potential impact of effective global TB control on health and economic outcomes in the United States. Methods: We estimated outcomes using linked mathematical models of TB epidemiology in the United States and migrants’ birth countries. A base-case scenario extrapolated country-specific TB incidence trends. We compared this with scenarios in which countries achieve 90% TB incidence reductions between 2015 and 2035, as targeted by the World Health Organization’s End TB Strategy (“effective global TB control”). We also considered pessimistic scenarios of flat TB incidence trends in individual countries. Measurements and Main Results: We estimated TB cases, deaths, and costs and the total economic burden of TB in the United States. Compared with the base-case scenario, effective global TB control would avert 40,000 (95% uncertainty interval, 29,000–55,000) TB cases in the United States in 2020–2035. TB incidence rates in 2035 would be 43% (95% uncertainty interval, 34–54%) lower than in the base-case scenario, and 49% (95% uncertainty interval, 44–55%) lower than in 2020. Summed over 2020–2035, this represents 0.8 billion dollars (95% uncertainty interval, 0.6–1.0 billion dollars) in averted healthcare costs and $2.5 billion dollars (95% uncertainty interval, 1.7–3.6 billion dollars) in productivity gains. The total U.S. economic burden of TB (including the value of averted TB deaths) would be 21% (95% uncertainty interval, 16–28%) lower (18 billion dollars [95% uncertainty level, 8–32 billion dollars]). Conclusions: In addition to producing major health benefits for high-burden countries, strengthened efforts to achieve effective global TB control could produce substantial health and economic benefits for the United States.

SummaryThe emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2500 COVID-19 cases associated with this variant have been detected in the US since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight the primary ports of entry for B.1.1.7 in the US and locations of possible underreporting of B.1.1.7 cases. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2500 COVID-19 cases associated with this variant have been detected in the US since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight that the primary ports of entry for B.1.1.7 in the US were in New York, California, and Florida. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response., The SARS-CoV-2 variant B.1.1.7 was introduced to the United States in early December 2020 and soon became established within many communities. The primary points of entry into the US are identified as NY, CA, and FL, and exponential growth in these states resulted in spread to neighboring states.

Air travel is an increasingly important conduit for the worldwide spread of infectious diseases. However, methods to identify which airports an individual may use to initiate travel, or where an individual may travel to upon arrival at an airport is not well studied. This knowledge gap can be addressed by estimating airport catchment areas: the geographic extent from which the airport derives most of its patronage. While airport catchment areas can provide a simple decision-support tool to help delineate the spatial extent of infectious disease spread at a local scale, observed data for airport catchment areas are rarely made publicly available. Therefore, we evaluated a probabilistic choice behavior model, the Huff model, as a potential methodology to estimate airport catchment areas in the United States in data-limited scenarios. We explored the impact of varying input parameters to the Huff model on estimated airport catchment areas: distance decay exponent, distance cut-off, and measures of airport attractiveness. We compared Huff model catchment area patterns for Miami International Airport (MIA) and Harrisburg International Airport (MDT). We specifically compared our model output to observed data sampled for MDT to align model parameters with an established, observed catchment area. Airport catchment areas derived using the Huff model were highly sensitive to changes in model parameters. We observed that a distance decay exponent of 2 and a distance cut-off of 500 km represented the most realistic spatial extent and heterogeneity of the MIA catchment area. When these parameters were applied to MDT, the Huff model produced similar spatial patterns to the observed MDT catchment area. Finally, our evaluation of airport attractiveness showed that travel volume to the specific international destinations of interest for infectious disease importation risks (i.e., Brazil) had little impact on the predicted choice of airport when compared to all international travel. Our work is a proof of concept for use of the Huff model to estimate airport catchment areas as a generalizable decision-support tool in data-limited scenarios. While our work represents an initial examination of the Huff model as a method to approximate airport catchment areas, an essential next step is to conduct a quantitative calibration and validation of the model based on multiple airports, possibly leveraging local human mobility data such as call detail records or online social network data collected from mobile devices. Ultimately, we demonstrate how the Huff model could be potentially helpful to improve the precision of early warning systems that anticipate infectious disease spread, or to incorporate when local public health decision makers need to identify where to mobilize screening infrastructure or containment strategies at a local level.

Zika virus belongs to the genus Flavivirus of the family Flaviviridae; it is transmitted to humans primarily through the bite of an infected Aedes species mosquito (e.g., Ae. aegypti and Ae. albopictus) (1). Zika virus has been identified as a cause of congenital microcephaly and other serious brain defects (2). As of June 30, 2016, CDC had issued travel notices for 49 countries and U.S. territories across much of the Western hemisphere (3), including Brazil, where the 2016 Olympic and Paralympic Games (Games of the XXXI Olympiad, also known as Rio 2016; Games) will be hosted in Rio de Janeiro in August and September 2016. During the Games, mosquito-borne Zika virus transmission is expected to be low because August and September are winter months in Brazil, when cooler and drier weather typically reduces mosquito populations (4). CDC conducted a risk assessment to predict those countries susceptible to ongoing Zika virus transmission resulting from introduction by a single traveler to the Games. Whereas all countries are at risk for travel-associated importation of Zika virus, CDC estimated that 19 countries currently not reporting Zika outbreaks have the environmental conditions and population susceptibility to sustain mosquito-borne transmission of Zika virus if a case were imported from infection at the Games. For 15 of these 19 countries, travel to Rio de Janeiro during the Games is not estimated to increase substantially the level of risk above that incurred by the usual aviation travel baseline for these countries. The remaining four countries, Chad, Djibouti, Eritrea, and Yemen, are unique in that they do not have a substantial number of travelers to any country with local Zika virus transmission, except for anticipated travel to the Games. These four countries will be represented by a projected, combined total of 19 athletes (plus a projected delegation of about 60 persons), a tiny fraction of the 350,000-500,000 visitors expected at the Games.* Overall travel volume to the Games represents a very small fraction (

Introduction Air, land, and sea transportation can facilitate rapid spread of infectious diseases. In May 2015 the Pan American Health Organization (PAHO) issued an alert regarding the first confirmed Zika virus infection in Brazil. As of March 8, 2016, the U.S. Centers for Disease Control and Prevention (CDC) had issued travel notices for 33 countries and 3 U.S. territories with local Zika virus transmission. Methods Using data from five separate datasets from 2014 and 2015, we estimated the annual number of passenger journeys by air and land border crossings to the United States from the 33 countries and 3 U.S. territories listed in the CDC's Zika travel notices as of March 8, 2016. We also estimated the annual number of passenger journeys originating in and returning to the United States (primarily on cruises) with visits to seaports in areas with local Zika virus transmission. Because of the adverse pregnancy and birth outcomes that have been associated with Zika virus disease, the number of passenger journeys completed by women of childbearing age and pregnant women was also estimated. Results An estimated 216.3 million passenger journeys by air, land, and sea are made annually to the United States from areas with local Zika virus transmission (as of March 8). The destination states with the largest numbers of arrivals were Texas (by land) and Florida (by air and sea). An estimated 51.7 million passenger journeys were made by women of childbearing age and an estimated 2.3 million were made by pregnant women. Conclusion Travel volume analyses provide important information that can be used to effectively target public health interventions as well as direct public health resources and efforts at local, regional, and country-specific levels.