Predictive power of air travel and socio-economic data for early pandemic spread

Zoonoses in a changing world

Animals are continuously exposed to pathogens but rarely get infected, because pathogens must overcome barriers to establish successful infections. Ongoing planetary changes affect factors relevant for such infections, such as pathogen pressure and pathogen exposure. The replacement of wildlife with domestic animals shrinks the original host reservoirs, whereas expanding agricultural frontiers lead to increased contact between natural and altered ecosystems, increasing pathogen exposure and reducing the area where the original hosts can live. Climate change alters species' distributions and phenology, pathogens included, resulting in exposure to pathogens that have colonized or recolonized new areas. Globalization leads to unwilling movement of and exposure to pathogens. Because people and domestic animals are overdominant planetwide, there is increased selective pressure for pathogens to infect them. Nature conservation measures can slow down but not fully prevent spillovers. Additional and enhanced surveillance methods in potential spillover hotspots should improve early detection and allow swifter responses to emerging outbreaks.

Airline strategies during the pandemic: What worked?

An examination is conducted of airline strategies during the covid-19 pandemic using data from the United States. Our findings show that airlines pursued diverse strategies in terms of route entry and retention, pricing, and load factors. At the route level, a more detailed examination is conducted of the performance of a middle-seat blocking strategy designed to increase the safety of air travel. We show that this strategy (i.e., not making middle seats available to passengers) likely resulted in revenue losses for carriers, an estimated US $3,300 per flight. This revenue loss provides an indication as to why the middle seat blocking strategy was discontinued by all US airlines despite ongoing safety concerns.

The effects of air transport mobility and global connectivity on viral transmission: Lessons learned from Covid-19 and its variants

We investigate the impact of air travel mobility and global connectivity on viral transmission by tracing the announced arrival time of COVID-19 and its major variants in countries around the world. We find that air travel intensity to a country, "effective distance" as measured by international air traffic, is generally a significant predictor for the announced viral arrival time. The level of healthcare infrastructure in a country is less important at predicting the initial transmission and detection time of a virus. A policy variable, notably the percentage reduction of total inbound seats in response to a viral outbreak, is largely ineffective at delaying viral transmission and discovery time. These findings suggest that air network connectivity is a major contributor to the speed of viral transmission. However, government attempts to delay viral transmission by reducing air network connectivity after the virus is first discovered are largely ineffective.

Causal connections between socioeconomic disparities and COVID-19 in the USA

With the increasing use of machine learning models in computational socioeconomics, the development of methods for explaining these models and understanding the causal connections is gradually gaining importance. In this work, we advocate the use of an explanatory framework from cooperative game theory augmented with do calculus, namely causal Shapley values. Using causal Shapley values, we analyze socioeconomic disparities that have a causal link to the spread of COVID-19 in the USA. We study several phases of the disease spread to show how the causal connections change over time. We perform a causal analysis using random effects models and discuss the correspondence between the two methods to verify our results. We show the distinct advantages a non-linear machine learning models have over linear models when performing a multivariate analysis, especially since the machine learning models can map out non-linear correlations in the data. In addition, the causal Shapley values allow for including the causal structure in the variable importance computed for the machine learning model.

Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: evidence from Bayesian model averaging

The COVID-19 pandemic resulted in great discrepancies in both infection and mortality rates between countries. Besides the biological and epidemiological factors, a multitude of social and economic criteria also influenced the extent to which these discrepancies appeared. Consequently, there is an active debate regarding the critical socio-economic and health factors that correlate with the infection and mortality rates outcome of the pandemic. Here, we leverage Bayesian model averaging techniques and country level data to investigate whether 28 variables, which describe a diverse set of health and socio-economic characteristics, correlate with the final number of infections and deaths during the first wave of the coronavirus pandemic. We show that only a few variables are able to robustly correlate with these outcomes. To understand the relationship between the potential correlates in explaining the infection and death rates, we create a Jointness Space. Using this space, we conclude that the extent to which each variable is able to provide a credible explanation for the COVID-19 infections/mortality outcome varies between countries because of their heterogeneous features.

Tracing the connections between international business and communicable diseases

We posit that international business and the emergence and spread of communicable diseases are intrinsically connected. To support our arguments, we first start with a historical timeline that traces the connections between international business and communicable diseases back to the sixth century. Second, following the epidemiology of communicable diseases, we identify two crucial transitions related to international business: the emergence of epidemics within a host country and the shift from epidemics to global pandemics. Third, we highlight international business contextual factors (host country regulatory quality, urbanization, trade barriers, global migration) and multinationals' activities (foreign direct investment, corporate political activity, global supply chain management, international travel) that could accelerate each transition. Finally, building on public health insights, we suggest research implications for business scholars on how to integrate human health challenges into their studies and practical implications for global managers on how to help prevent the emergence and spread of communicable diseases.

Estimating the potential for global dissemination of pandemic pathogens using the global airline network and healthcare development indices

Pandemics have the potential to incur significant health and economic impacts, and can reach a large number of countries from their origin within weeks. Early identification and containment of a newly emerged pandemic within the source country is key for minimising global impact. To identify a country's potential to control and contain a pathogen with pandemic potential, we compared the quality of a country's healthcare system against its global airline connectivity. Healthcare development was determined using three multi-factorial indices, while detailed airline passenger data was used to identify the global connectivity of all countries. Proximities of countries to a putative 'Worst Case Scenario' (extreme high-connectivity and low-healthcare development) were calculated. We found a positive relationship between a country's connectivity and healthcare metrics. We also identified countries that potentially pose the greatest risk for pandemic dissemination, notably Dominican Republic, India and Pakistan. China and Mexico, both sources of recent influenza and coronavirus pandemics were also identified as among the highest risk countries. Collectively, lower-middle and upper-middle income countries represented the greatest risk, while high income countries represented the lowest risk. Our analysis represents an alternative approach to identify countries where increased within-country disease surveillance and pandemic preparedness may benefit global health.

COVID-19-associated mortality across the countries of the Gulf Cooperation Council and how it compares to Europe: A comparative study

INTRODUCTION

In late 2019, a novel strain of coronavirus, discovered in the city of Wuhan, China, was found to cause a disease later named coronavirus disease 2019, or COVID-19. In January 2020, COVID-19 first reached the Gulf region. Afterwards, the disease spread rapidly across the countries of the Gulf and the number of COVID-19 cases rose significantly. Now, more than a year later, there are only a limited number of studies regarding COVID-19 and its behavior in this region. In this article, we aim to assess the mortality caused by the disease in the Gulf region by calculating the Case Fatality Rates (CFR) for all of the Gulf Cooperation countries and comparing the results with those of Europe.

METHODS

Data was obtained from the official statistics of the World Health Organization (WHO) from January to May 2020. From the data, the CFR was calculated for every Gulf and European country included in the study. Following the calculation, the results were compared and analyzed. To make our comparison more accurate, we added the total number of COVID-19 tests per 1000 population and the Health Access and Quality index for each individual country.

RESULTS

CFRs in the Gulf region to May 12, 2020 were: United Arab Emirates (1.06%), Kuwait (0.69%), Saudi Arabia (0.62%), Oman (0.45%), Bahrain (0.15%), and Qatar (0.06%). Within Europe over the same time period, 10 countries had CFRs above 10%, with the majority above 3%.

CONCLUSIONS

Compared to Europe, the COVID-19 mortality rate in the Gulf region has been much lower. The difference in age groups between the Gulf region and Europe may be the most important factor, mainly due to a younger population and a smaller elderly demographic in the Gulf region. Although age is a strong factor for the lower CFR in the Gulf, other factors must also be considered. These include the number of COVID-19 tests conducted per population, different country capabilities, and varying criteria for reporting COVID-19 deaths(Table-1)(Table-2).

Revisiting the Effects of High-Speed Railway Transfers in the Early COVID-19 Cross-Province Transmission in Mainland China

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic that was reported at the end of 2019 in Wuhan, China, and was rapidly disseminated to all provinces in around one month. The study aims to assess the changes in intercity railway passenger transport on the early spatial transmission of COVID-19 in mainland China. Examining the role of railway transport properties in disease transmission could help quantify the spatial spillover effects of large-scale travel restriction interventions. This study used daily high-speed railway schedule data to compare the differences in city-level network properties (destination arrival and transfer service) before and after the Wuhan city lockdown in the early stages of the spatial transmission of COVID-19 in mainland China. Bayesian multivariate regression was used to examine the association between structural changes in the railway origin-destination network and the incidence of COVID-19 cases. Our results show that the provinces with rising transfer activities after the Wuhan city lockdown had more confirmed COVID-19 cases, but changes in destination arrival did not have significant effects. The regions with increasing transfer activities were located in provinces neighboring Hubei in the widthwise and longitudinal directions. These results indicate that transfer activities enhance interpersonal transmission probability and could be a crucial risk factor for increasing epidemic severity after the Wuhan city lockdown. The destinations of railway passengers might not be affected by the Wuhan city lockdown, but their itinerary routes could be changed due to the replacement of an important transfer hub (Wuhan city) in the Chinese railway transportation network. As a result, transfer services in the high-speed rail network could explain why the provinces surrounded by Hubei had a higher number of confirmed COVID-19 cases than other provinces.

Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe

Often presented as a global pandemic spreading all over the world, COVID-19, however, hit not only countries but also regions differently. The objective of this paper is to focus on the spatial heterogeneity in the spread of the COVID-19 pandemic and to contribute to an understanding of the channels by which it spread, focusing on the regional socioeconomic dimension. For this, we use a dataset covering 125 European regions in 12 countries. Considering that the impact of the COVID-19 crisis differed sharply not only across countries but also across regions within the same country, the empirical strategy is based, on the one hand, on an exploratory analysis of spatial autocorrelations, which makes it possible to identify regional clusters of the disease. On the other hand, we use spatial regression models to capture the diffusion effect and the role of different families of regional factors in this process. We find that the share of older people in the population, GDP per capita, distance from achieving EU objectives, and the unemployment rate are correlated with high COVID-19 death rates. In contrast, the number of medical practitioners and hospital beds and the level of social trust are correlated with low COVID-19 death rates.

COVID-19 recovery rate and its association with development

Objectives:

The recovery rate is important to determine a country’s development towards controlling coronavirus. It is a function of myriad factors – death rate, cases requiring hospitalization, quality of care, and discharge policies, among others. India’s recovery rate is growing steadily from an earlier low of 10% to 11%. It is imperative to understand the determinants of recovery rate in a country to enable improvements in the same.

Material and Methods:

COVID-19 data have been compiled from several sources, including the Ministry of Health and Family Welfare, National Disaster Management Authority, and Indian Council of Medical Research and demographic and health data from Census of India, 2011, National Health Profile, 2019, and were used. The study uses linear regression to understand the relationship between recovery rate and development indicators in India.

Results:

Our analysis emphasizes the beneficial impacts of the health system and better economy on the recovery rate. Investment in health, urban stay, non-slum and non-poor population, and effective governance is instrumental in improving recovery rate.

Conclusion:

Scaling up health facilities and medical infrastructure, slum decongestion, focus on economically weaker sections, capacity building of health workers and ameliorating long-term investments in health, health research, and better quality of living are also essential to address recovery of COVID-19.

How does globalization affect COVID-19 responses?

BACKGROUND

The ongoing COVID-19 pandemic has highlighted the vast differences in approaches to the control and containment of coronavirus across the world and has demonstrated the varied success of such approaches in minimizing the transmission of coronavirus. While previous studies have demonstrated high predictive power of incorporating air travel data and governmental policy responses in global disease transmission modelling, factors influencing the decision to implement travel and border restriction policies have attracted relatively less attention. This paper examines the role of globalization on the pace of adoption of international travel-related non-pharmaceutical interventions (NPIs) during the coronavirus pandemic. This study aims to offer advice on how to improve the global planning, preparation, and coordination of actions and policy responses during future infectious disease outbreaks with empirical evidence.

METHODS AND DATA

We analyzed data on international travel restrictions in response to COVID-19 of 185 countries from January to October 2020. We applied time-to-event analysis to examine the relationship between globalization and the timing of travel restrictions implementation.

RESULTS

The results of our survival analysis suggest that, in general, more globalized countries, accounting for the country-specific timing of the virus outbreak and other factors, are more likely to adopt international travel restrictions policies. However, countries with high government effectiveness and globalization were more cautious in implementing travel restrictions, particularly if through formal political and trade policy integration. This finding is supported by a placebo analysis of domestic NPIs, where such a relationship is absent. Additionally, we find that globalized countries with high state capacity are more likely to have higher numbers of confirmed cases by the time a first restriction policy measure was taken.

CONCLUSIONS

The findings highlight the dynamic relationship between globalization and protectionism when governments respond to significant global events such as a public health crisis. We suggest that the observed caution of policy implementation by countries with high government efficiency and globalization is a by-product of commitment to existing trade agreements, a greater desire to 'learn from others' and also perhaps of 'confidence' in a government's ability to deal with a pandemic through its health system and state capacity. Our results suggest further research is warranted to explore whether global infectious disease forecasting could be improved by including the globalization index and in particular, the de jure economic and political, and de facto social dimensions of globalization, while accounting for the mediating role of government effectiveness. By acting as proxies for a countries' likelihood and speed of implementation for international travel restriction policies, such measures may predict the likely time delays in disease emergence and transmission across national borders.

The socioeconomic and environmental drivers of the COVID-19 pandemic: A review

In recent decades, there has been an intensification of the socioeconomic and environmental drivers of pandemics, including ecosystem conversion, meat consumption, urbanization, and connectivity among cities and countries. This paper reviews how these four systemic drivers help explain the dynamics of the COVID-19 pandemic and other recent emerging infectious diseases, and the policies that can be adopted to mitigate their risks. Land-use change and meat consumption increase the likelihood of pathogen spillover from animals to people. The risk that such zoonotic outbreaks will then spread to become pandemics is magnified by growing urban populations and the networks of trade and travel within and among countries. Zoonotic spillover can be mitigated through habitat protection and restrictions on the wildlife trade. Containing infectious disease spread requires a high degree of coordination among institutions across geographic jurisdictions and economic sectors, all backed by international investment and cooperation.

Pierre JM, Pickering TA, Demirjian NL, Fields BK, Desai B, Gholamrezanezhad A. Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country

Background: The novel Severe Acute Respiratory Syndrome Coronavirus-2 has led to a global pandemic in which case fatality rate (CFR) has varied from country to country. This study aims to identify factors that may explain the variation in CFR across countries. Methods: We identified 24 potential risk factors affecting CFR. For all countries with over 5000 reported COVID-19 cases, we used country-specific datasets from the WHO, the OECD, and the United Nations to quantify each of these factors. We examined univariable relationships of each variable with CFR, as well as correlations among predictors and potential interaction terms. Our final multivariable negative binomial model included univariable predictors of significance and all significant interaction terms. Results: Across the 39 countries under consideration, our model shows COVID-19 case fatality rate was best predicted by time to implementation of social distancing measures, hospital beds per 1000 individuals, percent population over 70 years, CT scanners per 1 million individuals, and (in countries with high population density) smoking prevalence. Conclusion: Our model predicted an increased CFR for countries that waited over 14 days to implement social distancing interventions after the 100th reported case. Smoking prevalence and percentage population over the age of 70 years were also associated with higher CFR. Hospital beds per 1000 and CT scanners per million were identified as possible protective factors associated with decreased CFR.

The influence of passenger air traffic on the spread of COVID-19 in the world

Countries in the world are suffering from COVID-19 and would like to control it. Thus, some authorities voted for new policies and even stopped passenger air traffic. Those decisions were not uniform, and this study focuses on how passenger air traffic might influence the spread of COVID-19 in the world. We used data sets of cases from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University and air transport (passengers carried) from the World Bank. Besides, we computed Poisson, QuasiPoisson, Negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models with cross-validation to make sure that our findings are robust. Actually, when passenger air traffic increases by one unit, the number of cases increases by one new infection.

Cross-Country Comparisons of Covid-19: Policy, Politics and the Price of Life

Coronavirus has claimed the lives of over half a million people world-wide and this death toll continues to rise rapidly each day. In the absence of a vaccine, non-clinical preventative measures have been implemented as the principal means of limiting deaths. However, these measures have caused unprecedented disruption to daily lives and economic activity. Given this developing crisis, the potential for a second wave of infections and the near certainty of future pandemics, lessons need to be rapidly gleaned from the available data. We address the challenges of cross-country comparisons by allowing for differences in reporting and variation in underlying socio-economic conditions between countries. Our analyses show that, to date, differences in policy interventions have out-weighed socio-economic variation in explaining the range of death rates observed in the data. Our epidemiological models show that across 8 countries a further week long delay in imposing lockdown would likely have cost more than half a million lives. Furthermore, those countries which acted more promptly saved substantially more lives than those that delayed. Linking decisions over the timing of lockdown and consequent deaths to economic data, we reveal the costs that national governments were implicitly prepared to pay to protect their citizens as reflected in the economic activity foregone to save lives. These 'price of life' estimates vary enormously between countries, ranging from as low as around $100,000 (e.g. the UK, US and Italy) to in excess of $1million (e.g. Denmark, Germany, New Zealand and Korea). The lowest estimates are further reduced once we correct for under-reporting of Covid-19 deaths.

The Predictive Capacity of Air Travel Patterns During the Global Spread of the COVID-19 Pandemic: Risk, Uncertainty and Randomness

Air travel has a decisive role in the spread of infectious diseases at the global level. We present a methodology applied during the early stages of the COVID-19 pandemic that uses detailed aviation data at the final destination level in order to measure the risk of the disease spreading outside China. The approach proved to be successful in terms of identifying countries with a high risk of infected travellers and as a tool to monitor the evolution of the pandemic in different countries. The high number of undetected or asymptomatic cases of COVID-19, however, limits the capacity of the approach to model the full dynamics. As a result, the risk for countries with a low number of passengers from Hubei province appeared as low. Globalization and international aviation connectivity allow travel times that are much shorter than the incubation period of infectious diseases, a fact that raises the question of how to react in a potential new pandemic.

Cross-Country Comparisons of Covid-19: Policy, Politics and the Price of Life

Coronavirus has claimed the lives of over half a million people world-wide and this death toll continues to rise rapidly each day. In the absence of a vaccine, non-clinical preventative measures have been implemented as the principal means of limiting deaths. However, these measures have caused unprecedented disruption to daily lives and economic activity. Given this developing crisis, the potential for a second wave of infections and the near certainty of future pandemics, lessons need to be rapidly gleaned from the available data. We address the challenges of cross-country comparisons by allowing for differences in reporting and variation in underlying socio-economic conditions between countries. Our analyses show that, to date, differences in policy interventions have out-weighed socio-economic variation in explaining the range of death rates observed in the data. Our epidemiological models show that across 8 countries a further week long delay in imposing lockdown would likely have cost more than half a million lives. Furthermore, those countries which acted more promptly saved substantially more lives than those that delayed. Linking decisions over the timing of lockdown and consequent deaths to economic data, we reveal the costs that national governments were implicitly prepared to pay to protect their citizens as reflected in the economic activity foregone to save lives. These 'price of life' estimates vary enormously between countries, ranging from as low as around $100,000 (e.g. the UK, US and Italy) to in excess of $1million (e.g. Denmark, Germany, New Zealand and Korea). The lowest estimates are further reduced once we correct for under-reporting of Covid-19 deaths.

The use and reporting of airline passenger data for infectious disease modelling: a systematic review

BackgroundA variety of airline passenger data sources are used for modelling the international spread of infectious diseases. Questions exist regarding the suitability and validity of these sources.AimWe conducted a systematic review to identify the sources of airline passenger data used for these purposes and to assess validation of the data and reproducibility of the methodology.MethodsArticles matching our search criteria and describing a model of the international spread of human infectious disease, parameterised with airline passenger data, were identified. Information regarding type and source of airline passenger data used was collated and the studies' reproducibility assessed.ResultsWe identified 136 articles. The majority (n = 96) sourced data primarily used by the airline industry. Governmental data sources were used in 30 studies and data published by individual airports in four studies. Validation of passenger data was conducted in only seven studies. No study was found to be fully reproducible, although eight were partially reproducible.LimitationsBy limiting the articles to international spread, articles focussed on within-country transmission even if they used relevant data sources were excluded. Authors were not contacted to clarify their methods. Searches were limited to articles in PubMed, Web of Science and Scopus.ConclusionWe recommend greater efforts to assess validity and biases of airline passenger data used for modelling studies, particularly when model outputs are to inform national and international public health policies. We also recommend improving reporting standards and more detailed studies on biases in commercial and open-access data to assess their reproducibility.

Emerging human infectious diseases and the links to global food production

Infectious diseases are emerging globally at an unprecedented rate while global food demand is projected to increase sharply by 2100. Here, we synthesize the pathways by which projected agricultural expansion and intensification will influence human infectious diseases and how human infectious diseases might likewise affect food production and distribution. Feeding 11 billion people will require substantial increases in crop and animal production that will expand agricultural use of antibiotics, water, pesticides and fertilizer, and contact rates between humans and both wild and domestic animals, all with consequences for the emergence and spread of infectious agents. Indeed, our synthesis of the literature suggests that, since 1940, agricultural drivers were associated with >25% of all - and >50% of zoonotic - infectious diseases that emerged in humans, proportions that will likely increase as agriculture expands and intensifies. We identify agricultural and disease management and policy actions, and additional research, needed to address the public health challenge posed by feeding 11 billion people.

Roles of Different Transport Modes in the Spatial Spread of the 2009 Influenza A(H1N1) Pandemic in Mainland China

There is increasing concern about another influenza pandemic in China. However, the understanding of the roles of transport modes in the 2009 influenza A(H1N1) pandemic spread across mainland China is limited. Herein, we collected 127,797 laboratory-confirmed cases of influenza A(H1N1)pdm09 in mainland China from May 2009 to April 2010. Arrival days and peak days were calculated for all 340 prefectures to characterize the dissemination patterns of the pandemic. We first evaluated the effects of airports and railway stations on arrival days and peak days, and then we applied quantile regressions to quantify the relationships between arrival days and air, rail, and road travel. Our results showed that early arrival of the virus was not associated with an early incidence peak. Airports and railway stations in prefectures significantly advanced arrival days but had no significant impact on peak days. The pandemic spread across mainland China from the southeast to the northwest in two phases that were split at approximately 1 August 2009. Both air and road travel played a significant role in accelerating the spread during phases I and II, but rail travel was only significant during phase II. In conclusion, in addition to air and road travel, rail travel also played a significant role in accelerating influenza A(H1N1)pdm09 spread between prefectures. Establishing a multiscale mobility network that considers the competitive advantage of rail travel for mid to long distances is essential for understanding the influenza pandemic transmission in China.

Managing the emergence of pathogen resistance via spatially targeted antimicrobial use

From agriculture to public health to civil engineering, managing antimicrobial resistance presents a considerable challenge. The dynamics underlying resistance evolution reflect inherently spatial processes. Resistant pathogen strains increase in frequency when a strain that emerges in one locale can spread and replace pathogen subpopulations formerly sensitive to the antimicrobial agent. Moreover, the strength of selection for antimicrobial resistance is in part governed by the extent of antimicrobial use. Thus, altering how antimicrobials are used across a landscape can potentially shift the spatial context governing the dynamics of antimicrobial resistance and provide a potent management tool. Here, we model how the efficacy of adjusting antimicrobial use over space to manage antimicrobial resistance is mediated by competition among pathogen strains and the topology of pathogen metapopulations. For several pathogen migration scenarios, we derive critical thresholds for the spatial extent of antimicrobial use below which resistance cannot emerge, and relate these thresholds to (a) the ability to eradicate antimicrobial-sensitive pathogens locally and (b) the strength of the trade-off between resistance ability and competitive performance where antimicrobial use is absent. We find that in metapopulations where patches differ in connectedness, constraining antimicrobial use across space to mitigate resistance evolution only works if the migration of the resistant pathogen is modest; yet, this situation is reversed if the resistant strain has a high colonization rate, with variably connected metapopulations exhibiting less sensitivity to reducing antimicrobial use across space. Furthermore, when pathogens are alternately exposed to sites with and without the antimicrobial, bottlenecking resistant strains through sites without an antimicrobial is only likely to be effective under a strong competition-resistance trade-off. We therefore identify life-history constraints that are likely to suggest which pathogens can most effectively be controlled by a spatially targeted antimicrobial regime. We discuss implications of our results for managing and thinking about antimicrobial resistance evolution in spatially heterogeneous contexts.

The Economic Case for a Pandemic Fund

The rapid urban spread of Ebola virus in West Africa in 2014 and consequent breakdown of control measures led to a significant economic impact as well as the burden on public health and wellbeing. The US government appropriated $5.4 Billion for FY2015 and WHO proposed a $100 Million emergency fund largely to curtail the threat of future outbreaks. Using epidemiological analyses and economic modeling, we propose that the best use of these and similar funds would be to serve as global insurance against the continued threat of emerging infectious diseases. An effective strategy would involve the initial investment in strengthening mobile and adaptable capacity to deal with the threat and reality of disease emergence, coupled with repeated investment to maintain what is effectively a 'national guard' for pandemic prevention and response. This investment would create a capital stock that could also provide access to safe treatment during and between crises in developing countries, lowering risk to developed countries.

Possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 - a global mortality impact modeling study

BACKGROUND

A global pandemic mortality study found prominent regional mortality variations in 2009 for Influenza A(H1N1)pdm09. Our study attempts to identify factors that explain why the pandemic mortality burden was high in some countries and low in others.

METHODS

As a starting point, we identified possible risk factors worth investigating for Influenza A(H1N1)pdm09 mortality through a targeted literature search. We then used a modeling procedure (data simulations and regression models) to identify factors that could explain differences in respiratory mortality due to Influenza A(H1N1)pdm09. We ran sixteen models to produce robust results and draw conclusions. In order to assess the role of each factor in explaining differences in excess pandemic mortality, we calculated the reduction in between country variance, which can be viewed as an effect-size for each factor.

RESULTS

The literature search identified 124 publications and 48 possible risk factors, of which we were able to identify 27 factors with appropriate global datasets. The modelling procedure indicated that age structure (explaining 40% of the mean between country variance), latitude (8%), influenza A and B viruses circulating during the pandemic (3-8%), influenza A and B viruses circulating during the preceding influenza season (2-6%), air pollution (pm10; 4%) and the prevalence of other infections (HIV and TB) (4-6%) were factors that explained differences in mortality around the world. Healthcare expenditure, levels of obesity, the distribution of antivirals, and air travel did not explain global pandemic mortality differences.

CONCLUSIONS

Our study found that countries with a large proportion of young persons had higher pandemic mortality rates in 2009. The co-circulation of influenza viruses during the pandemic and the circulation of influenza viruses during the preceding season were also associated with pandemic mortality rates. We found that real time assessments of 2009 pandemic mortality risk factors (e.g. obesity) probably led to a number of false positive findings.

Continuous cell lines from the Muscovy duck as potential replacement for primary cells in the production of avian vaccines

Veterinary vaccines contribute to food security, interrupt zoonotic transmissions, and help to maintain overall health in livestock. Although vaccines are usually cost-effective, their adoption depends on a multitude of factors. Because poultry vaccines are usually given to birds with a short life span, very low production cost per dose is one important challenge. Other hurdles are to ensure a consistent and reliable supply of very large number of doses, and to have flexible production processes to accommodate a range of different pathogens and dosage requirements. Most poultry vaccines are currently being produced on primary avian cells derived from chicken or waterfowl embryos. This production system is associated with high costs, logistic complexities, rigid intervals between harvest and production, and supply limitations. We investigated whether the continuous cell lines Cairina retina and CR.pIX may provide a substrate independent of primary cell cultures or embryonated eggs. Viruses examined for replication in these cell lines are strains associated with, or contained in vaccines against egg drop syndrome, Marek's disease, Newcastle disease, avian influenza, infectious bursal disease and Derzsy's disease. Each of the tested viruses required the development of unique conditions for replication that are described here and can be used to generate material for in vivo efficacy studies and to accelerate transfer of the processes to larger production volumes.

Evaluation and Verification of the Global Rapid Identification of Threats System for Infectious Diseases in Textual Data Sources

The Global Rapid Identification of Threats System (GRITS) is a biosurveillance application that enables infectious disease analysts to monitor nontraditional information sources (e.g., social media, online news outlets, ProMED-mail reports, and blogs) for infectious disease threats. GRITS analyzes these textual data sources by identifying, extracting, and succinctly visualizing epidemiologic information and suggests potentially associated infectious diseases. This manuscript evaluates and verifies the diagnoses that GRITS performs and discusses novel aspects of the software package. Via GRITS' web interface, infectious disease analysts can examine dynamic visualizations of GRITS' analyses and explore historical infectious disease emergence events. The GRITS API can be used to continuously analyze information feeds, and the API enables GRITS technology to be easily incorporated into other biosurveillance systems. GRITS is a flexible tool that can be modified to conduct sophisticated medical report triaging, expanded to include customized alert systems, and tailored to address other biosurveillance needs.

International Air Travel to Ohio, USA, and the Impact on Malaria, Influenza, and Hepatitis A

The State of Ohio led the United States in measles in 2014, ostensibly related to international air travel (IAT), and ranked lower than 43 other states in infectious disease outbreak preparedness. We conducted a retrospective cohort study using surveillance data of the total Ohio population of 11 million from 2010 through 2014 with a nested case control of air travelers to determine the risk of malaria, seasonal influenza hospitalizations (IH), and hepatitis A (HA) disease related to international travel and to estimate the association with domestic enplanement. IAT appeared protective for HA and IH with a risk of 0.031 (.02-.04) but for malaria was 2.7 (2.07-3.62). Enplanement increased the risk for nonendemic M 3.5 (2.5-4.9) and for HA and IH 1.39 (1.34-1.44). IAT's ratio of relative risk (RRR) of malaria to HA and IH was 87.1 (55.8-136) greater than 219 times versus domestic enplanement which was protective for malaria at 0.397 (0.282-0.559). Malaria is correlated with IAT with cases increasing by 6.9 for every 10,000 passports issued.

Global biogeography of human infectious diseases

The distributions of most infectious agents causing disease in humans are poorly resolved or unknown. However, poorly known and unknown agents contribute to the global burden of disease and will underlie many future disease risks. Existing patterns of infectious disease co-occurrence could thus play a critical role in resolving or anticipating current and future disease threats. We analyzed the global occurrence patterns of 187 human infectious diseases across 225 countries and seven epidemiological classes (human-specific, zoonotic, vector-borne, non-vector-borne, bacterial, viral, and parasitic) to show that human infectious diseases exhibit distinct spatial grouping patterns at a global scale. We demonstrate, using outbreaks of Ebola virus as a test case, that this spatial structuring provides an untapped source of prior information that could be used to tighten the focus of a range of health-related research and management activities at early stages or in data-poor settings, including disease surveillance, outbreak responses, or optimizing pathogen discovery. In examining the correlates of these spatial patterns, among a range of geographic, epidemiological, environmental, and social factors, mammalian biodiversity was the strongest predictor of infectious disease co-occurrence overall and for six of the seven disease classes examined, giving rise to a striking congruence between global pathogeographic and "Wallacean" zoogeographic patterns. This clear biogeographic signal suggests that infectious disease assemblages remain fundamentally constrained in their distributions by ecological barriers to dispersal or establishment, despite the homogenizing forces of globalization. Pathogeography thus provides an overarching context in which other factors promoting infectious disease emergence and spread are set.

New technologies in predicting, preventing and controlling emerging infectious diseases

Surveillance of emerging infectious diseases is vital for the early identification of public health threats. Emergence of novel infections is linked to human factors such as population density, travel and trade and ecological factors like climate change and agricultural practices. A wealth of new technologies is becoming increasingly available for the rapid molecular identification of pathogens but also for the more accurate monitoring of infectious disease activity. Web-based surveillance tools and epidemic intelligence methods, used by all major public health institutions, are intended to facilitate risk assessment and timely outbreak detection. In this review, we present new methods for regional and global infectious disease surveillance and advances in epidemic modeling aimed to predict and prevent future infectious diseases threats.

Early vaccine availability represents an important public health advance for the control of pandemic influenza

BACKGROUND

Traditional processes for the production of pandemic influenza vaccines are not capable of producing a vaccine that could be deployed sooner than 5-6 months after strain identification. Plant-based vaccine technologies are of public health interest because they represent an opportunity to begin vaccinating earlier.

METHODS

We used an age- and risk- structured disease transmission model for Canada to evaluate the potential impact of a plant-produced vaccine available for rapid deployment (within 1-3 months) compared to an egg-based vaccine timeline.

RESULTS

We found that in the case of a mildly transmissible virus (R0 = 1.3), depending on the amount of plant-based vaccine produced per week, severe clinical outcomes could be decreased by 60-100 % if vaccine was available within 3 months of strain identification. However, in the case of a highly transmissible virus (R0 = 2.0), a delay of 3 months does not change clinical outcomes regardless of the level of weekly vaccine availability. If transmissibility is high, the only strategy that can impact clinical outcomes occurs if vaccine production is high and available within 2 months.

CONCLUSIONS

Pandemic influenza vaccines produced by plants, change the timeline of pandemic vaccine availability in a way that could significantly mitigate the impact of the next influenza pandemic.

Phylodynamic analysis of avian infectious bronchitis virus in South America

Infectious bronchitis virus (IBV) is a coronavirus of chickens that causes great economic losses to the global poultry industry. The present study focuses on South American IBVs and their genetic relationships with global strains. We obtained full-length sequences of the S1 coding region and N gene of IBV field isolates from Uruguay and Argentina, and performed Phylodynamic analysis to characterize the strains and estimate the time of the most recent common ancestor. We identified two major South American genotypes, which were here denoted South America I (SAI) and Asia/South America II (A/SAII). The SAI genotype is an exclusive South American lineage that emerged in the 1960s. The A/SAII genotype may have emerged in Asia in approximately 1995 before being introduced into South America. Both SAI and A/SAII genotype strains clearly differ from the Massachusetts strains that are included in the vaccine formulations being used in most South American countries.

Integrating invasion and disease in the risk assessment of live bird trade

AIM

International trade in plants and animals generates significant economic benefits. It also leads to substantial unintended impacts when introduced species become invasive, causing environmental disturbance or transmitting diseases that affect people, livestock, other wildlife or the environment. Policy responses are usually only implemented after these species become established and damages are already incurred. International agreements to control trade are likewise usually based on selection of species with known impacts. We aim to further develop quantitative invasive species risk assessment for bird imports and extend the tool to explicitly address disease threats.

LOCATION

United States of America.

METHODS

We use a two-step approach for rapid risk assessment based on the expected biological risks due to both the environmental and health impact of a potentially invasive wildlife species in trade. We assess establishment probability based on a model informed by historical observations and then construct a model of emerging infectious disease threat based on economic and ecological characteristics of the exporting country.

RESULTS

We illustrate how our rapid assessment tool can be used to identify high-priority species for regulation based on a combination of the threat they pose for becoming established and vectoring emerging infectious diseases.

MAIN CONCLUSIONS

Our approach can be executed for a species in a matter of days and is nested in an economic decision-making framework for determining whether the biological risk is justified by trade benefits.

Environmental role in influenza virus outbreaks

The environmental drivers of influenza outbreaks are largely unknown. Despite more than 50 years of research, there are conflicting lines of evidence on the role of the environment in influenza A virus (IAV) survival, stability, and transmissibility. With the increasing and looming threat of pandemic influenza, it is important to understand these factors for early intervention and long-term control strategies. The factors that dictate the severity and spread of influenza would include the virus, natural and acquired hosts, virus-host interactions, environmental persistence, virus stability and transmissibility, and anthropogenic interventions. Virus persistence in different environments is subject to minor variations in temperature, humidity, pH, salinity, air pollution, and solar radiations. Seasonality of influenza is largely dictated by temperature and humidity, with cool-dry conditions enhancing IAV survival and transmissibility in temperate climates in high latitudes, whereas humid-rainy conditions favor outbreaks in low latitudes, as seen in tropical and subtropical zones. In mid-latitudes, semiannual outbreaks result from alternating cool-dry and humid-rainy conditions. The mechanism of virus survival in the cool-dry or humid-rainy conditions is largely determined by the presence of salts and proteins in the respiratory droplets. Social determinants of heath, including health equity, vaccine acceptance, and age-related illness, may play a role in influenza occurrence and spread.

The dawn of Structural One Health: a new science tracking disease emergence along circuits of capital

The One Health approach integrates health investigations across the tree of life, including, but not limited to, wildlife, livestock, crops, and humans. It redresses an epistemological alienation at the heart of much modern population health, which has long segregated studies by species. Up to this point, however, One Health research has also omitted addressing fundamental structural causes underlying collapsing health ecologies. In this critical review we unpack the relationship between One Health science and its political economy, particularly the conceptual and methodological trajectories by which it fails to incorporate social determinants of epizootic spillover. We also introduce a Structural One Health that addresses the research gap. The new science, open to incorporating developments across the social sciences, addresses foundational processes underlying multispecies health, including the place-specific deep-time histories, cultural infrastructure, and economic geographies driving disease emergence. We introduce an ongoing project on avian influenza to illustrate Structural One Health's scope and ambition. For the first time researchers are quantifying the relationships among transnational circuits of capital, associated shifts in agroecological landscapes, and the genetic evolution and spatial spread of a xenospecific pathogen.

Spatial Transmission of 2009 Pandemic Influenza in the US

The 2009 H1N1 influenza pandemic provides a unique opportunity for detailed examination of the spatial dynamics of an emerging pathogen. In the US, the pandemic was characterized by substantial geographical heterogeneity: the 2009 spring wave was limited mainly to northeastern cities while the larger fall wave affected the whole country. Here we use finely resolved spatial and temporal influenza disease data based on electronic medical claims to explore the spread of the fall pandemic wave across 271 US cities and associated suburban areas. We document a clear spatial pattern in the timing of onset of the fall wave, starting in southeastern cities and spreading outwards over a period of three months. We use mechanistic models to tease apart the external factors associated with the timing of the fall wave arrival: differential seeding events linked to demographic factors, school opening dates, absolute humidity, prior immunity from the spring wave, spatial diffusion, and their interactions. Although the onset of the fall wave was correlated with school openings as previously reported, models including spatial spread alone resulted in better fit. The best model had a combination of the two. Absolute humidity or prior exposure during the spring wave did not improve the fit and population size only played a weak role. In conclusion, the protracted spread of pandemic influenza in fall 2009 in the US was dominated by short-distance spatial spread partially catalysed by school openings rather than long-distance transmission events. This is in contrast to the rapid hierarchical transmission patterns previously described for seasonal influenza. The findings underline the critical role that school-age children play in facilitating the geographic spread of pandemic influenza and highlight the need for further information on the movement and mixing patterns of this age group.

Influenza forecasting in human populations: a scoping review

Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms “influenza AND (forecast* OR predict*)”, excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials.

Pathogens, pests, and economics: drivers of honey bee colony declines and losses

The Western honey bee (Apis mellifera) is responsible for ecosystem services (pollination) worth US$215 billion annually worldwide and the number of managed colonies has increased 45% since 1961. However, in Europe and the U.S., two distinct phenomena; long-term declines in colony numbers and increasing annual colony losses, have led to significant interest in their causes and environmental implications. The most important drivers of a long-term decline in colony numbers appear to be socioeconomic and political pressure on honey production. In contrast, annual colony losses seem to be driven mainly by the spread of introduced pathogens and pests, and management problems due to a long-term intensification of production and the transition from large numbers of small apiaries to fewer, larger operations. We conclude that, while other causal hypotheses have received substantial interest, the role of pests, pathogens, and management issues requires increased attention.

Contrasting the epidemiological and evolutionary dynamics of influenza spatial transmission

In the past decade, rapid increases in the availability of high-resolution molecular and epidemiological data, combined with developments in statistical and computational methods to simulate and infer migration patterns, have provided key insights into the spatial dynamics of influenza A viruses in humans. In this review, we contrast findings from epidemiological and molecular studies of influenza virus transmission at different spatial scales. We show that findings are broadly consistent in large-scale studies of inter-regional or inter-hemispheric spread in temperate regions, revealing intense epidemics associated with multiple viral introductions, followed by deep troughs driven by seasonal bottlenecks. However, aspects of the global transmission dynamics of influenza viruses are still debated, especially with respect to the existence of tropical source populations experiencing high levels of genetic diversity and the extent of prolonged viral persistence between epidemics. At the scale of a country or community, epidemiological studies have revealed spatially structured diffusion patterns in seasonal and pandemic outbreaks, which were not identified in molecular studies. We discuss the role of sampling issues in generating these conflicting results, and suggest strategies for future research that may help to fully integrate the epidemiological and evolutionary dynamics of influenza virus over space and time.

The changing face of pathogen discovery and surveillance

The pace of pathogen discovery is rapidly accelerating. This reflects not only factors that enable the appearance and globalization of new microbial infections, but also improvements in methods for ascertaining the cause of a new disease. Innovative molecular diagnostic platforms, investments in pathogen surveillance (in wildlife, domestic animals and humans) and the advent of social media tools that mine the World Wide Web for clues indicating the occurrence of infectious-disease outbreaks are all proving to be invaluable for the early recognition of threats to public health. In addition, models of microbial pathogenesis are becoming more complex, providing insights into the mechanisms by which microorganisms can contribute to chronic illnesses like cancer, peptic ulcer disease and mental illness. Here, I review the factors that contribute to infectious-disease emergence, as well as strategies for addressing the challenges of pathogen surveillance and discovery.

Emerging virus diseases: can we ever expect the unexpected?

Emerging virus diseases are a major threat to human and veterinary public health. With new examples occurring approximately one each year, the majority are viruses originating from an animal host. Of the many factors responsible, changes to local ecosystems that perturb the balance between pathogen and principal host species is one of the major drivers, together with increasing urbanization of mankind and changes in human behavior. Many emerging viruses have RNA genomes and as such are capable of rapid mutation and selection of new variants in the face of environmental changes in host numbers and available target species. This review summarizes recent work on aspects of virus emergence and the current understanding of the molecular and immunological basis whereby viruses may cross between species and become established in new ecological niches. Emergence is hard to predict, although mathematical modeling and spatial epidemiology have done much to improve the prediction of where emergence may occur. However, much needs to be done to ensure adequate surveillance is maintained of animal species known to present the greatest risk thus increasing general alertness among physicians, veterinarians and those responsible for formulating public health policy.

Prediction and prevention of the next pandemic zoonosis

Most pandemics--eg, HIV/AIDS, severe acute respiratory syndrome, pandemic influenza--originate 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.

Forecasting high-priority infectious disease surveillance regions: a socioeconomic model

BACKGROUND

Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks.

METHODS

We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates.

RESULTS

Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996-2008.

CONCLUSIONS

Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions.

Interdisciplinary approaches to understanding disease emergence: the past, present, and future drivers of Nipah virus emergence

Emerging infectious diseases (EIDs) pose a significant threat to human health, economic stability, and biodiversity. Despite this, the mechanisms underlying disease emergence are still not fully understood, and control measures rely heavily on mitigating the impact of EIDs after they have emerged. Here, we highlight the emergence of a zoonotic Henipavirus, Nipah virus, to demonstrate the interdisciplinary and macroecological approaches necessary to understand EID emergence. Previous work suggests that Nipah virus emerged due to the interaction of the wildlife reservoir (Pteropus spp. fruit bats) with intensively managed livestock. The emergence of this and other henipaviruses involves interactions among a suite of anthropogenic environmental changes, socioeconomic factors, and changes in demography that overlay and interact with the distribution of these pathogens in their wildlife reservoirs. Here, we demonstrate how ecological niche modeling may be used to investigate the potential role of a changing climate on the future risk for Henipavirus emergence. We show that the distribution of Henipavirus reservoirs, and therefore henipaviruses, will likely change under climate change scenarios, a fundamental precondition for disease emergence in humans. We assess the variation among climate models to estimate where Henipavirus host distribution is most likely to expand, contract, or remain stable, presenting new risks for human health. We conclude that there is substantial potential to use this modeling framework to explore the distribution of wildlife hosts under a changing climate. These approaches may directly inform current and future management and surveillance strategies aiming to improve pathogen detection and, ultimately, reduce emergence risk.

Air travel and vector-borne disease movement

Recent decades have seen substantial expansions in the global air travel network and rapid increases in traffic volumes. The effects of this are well studied in terms of the spread of directly transmitted infections, but the role of air travel in the movement of vector-borne diseases is less well understood. Increasingly however, wider reaching surveillance for vector-borne diseases and our improving abilities to map the distributions of vectors and the diseases they carry, are providing opportunities to better our understanding of the impact of increasing air travel. Here we examine global trends in the continued expansion of air transport and its impact upon epidemiology. Novel malaria and chikungunya examples are presented, detailing how geospatial data in combination with information on air traffic can be used to predict the risks of vector-borne disease importation and establishment. Finally, we describe the development of an online tool, the Vector-Borne Disease Airline Importation Risk (VBD-Air) tool, which brings together spatial data on air traffic and vector-borne disease distributions to quantify the seasonally changing risks for importation to non-endemic regions. Such a framework provides the first steps towards an ultimate goal of adaptive management based on near real time flight data and vector-borne disease surveillance.

The ecology of emerging infectious diseases in migratory birds: an assessment of the role of climate change and priorities for future research

Pathogens that are maintained by wild birds occasionally jump to human hosts, causing considerable loss of life and disruption to global commerce. Preliminary evidence suggests that climate change and human movements and commerce may have played a role in recent range expansions of avian pathogens. Since the magnitude of climate change in the coming decades is predicted to exceed climatic changes in the recent past, there is an urgent need to determine the extent to which climate change may drive the spread of disease by avian migrants. In this review, we recommend actions intended to mitigate the impact of emergent pathogens of migratory birds on biodiversity and public health. Increased surveillance that builds upon existing bird banding networks is required to conclusively establish a link between climate and avian pathogens and to prevent pathogens with migratory bird reservoirs from spilling over to humans.

A model-based tool to predict the propagation of infectious disease via airports

Epidemics of novel or re-emerging infectious diseases have quickly spread globally via air travel, as highlighted by pandemic H1N1 influenza in 2009 (pH1N1). Federal, state, and local public health responders must be able to plan for and respond to these events at aviation points of entry. The emergence of a novel influenza virus and its spread to the United States were simulated for February 2009 from 55 international metropolitan areas using three basic reproduction numbers (R(0)): 1.53, 1.70, and 1.90. Empirical data from the pH1N1 virus were used to validate our SEIR model. Time to entry to the U.S. during the early stages of a prototypical novel communicable disease was predicted based on the aviation network patterns and the epidemiology of the disease. For example, approximately 96% of origins (R(0) of 1.53) propagated a disease into the U.S. in under 75 days, 90% of these origins propagated a disease in under 50 days. An R(0) of 1.53 reproduced the pH1NI observations. The ability to anticipate the rate and location of disease introduction into the U.S. provides greater opportunity to plan responses based on the scenario as it is unfolding. This simulation tool can aid public health officials to assess risk and leverage resources efficiently.

Searching for the most cost-effective strategy for controlling epidemics spreading on regular and small-world networks

We present a combined epidemiological and economic model for control of diseases spreading on local and small-world networks. The disease is characterized by a pre-symptomatic infectious stage that makes detection and control of cases more difficult. The effectiveness of local (ring-vaccination or culling) and global control strategies is analysed by comparing the net present values of the combined cost of preventive treatment and illness. The optimal strategy is then selected by minimizing the total cost of the epidemic. We show that three main strategies emerge, with treating a large number of individuals (global strategy, GS), treating a small number of individuals in a well-defined neighbourhood of a detected case (local strategy) and allowing the disease to spread unchecked (null strategy, NS). The choice of the optimal strategy is governed mainly by a relative cost of palliative and preventive treatments. If the disease spreads within the well-defined neighbourhood, the local strategy is optimal unless the cost of a single vaccine is much higher than the cost associated with hospitalization. In the latter case, it is most cost-effective to refrain from prevention. Destruction of local correlations, either by long-range (small-world) links or by inclusion of many initial foci, expands the range of costs for which the NS is most cost-effective. The GS emerges for the case when the cost of prevention is much lower than the cost of treatment and there is a substantial non-local component in the disease spread. We also show that local treatment is only desirable if the disease spreads on a small-world network with sufficiently few long-range links; otherwise it is optimal to treat globally. In the mean-field case, there are only two optimal solutions, to treat all if the cost of the vaccine is low and to treat nobody if it is high. The basic reproduction ratio, R(0), does not depend on the rate of responsive treatment in this case and the disease always invades (but might be stopped afterwards). The details of the local control strategy, and in particular the optimal size of the control neighbourhood, are determined by the epidemiology of the disease. The properties of the pathogen might not be known in advance for emerging diseases, but the broad choice of the strategy can be made based on economic analysis only.

Human vs. animal outbreaks of the 2009 swine-origin H1N1 influenza A epidemic

The majority of emerging infectious diseases are zoonotic in origin, including recently emerging influenza viruses such as the 2009 swine-origin H1N1 influenza A epidemic. The epidemic that year affected both human and animal populations as it spread globally. In fact, before the end of 2009, 14 different countries reported H1N1 infected swine. In order to better understand the zoonotic nature of the epidemic and the relationship between human and animal disease surveillance data streams, we compared 2009 reports of H1N1 infection to define the temporal relationship between reported cases in animals and humans. Generally, human cases preceded animal cases at a country-level, supporting the potential of H1N1 infection to be a "reverse zoonosis", and the value of integrating human and animal disease report data.

Emerging and neglected tropical diseases: translational application of proteomics

The challenges of identifying and controlling emerging diseases impact individual health, as well as political, social and economic situations. In this review we discuss the role of proteomics for investigation of pathogen discovery, outbreak investigation, bio-defense, disease control, host-pathogen dynamics and vaccine development of emerging and neglected tropical diseases (NTDs). In the future the discipline of proteomics may help define multiple aspects of emerging and NTDs with respect to personalized medicine and public health.