Modeling rapidly disseminating infectious disease during mass gatherings

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Prevention and control of Zika fever as a mosquito-borne and sexually transmitted disease

The ongoing Zika virus (ZIKV) epidemic poses a major global public health emergency. It is known that ZIKV is spread by \textit{Aedes} mosquitoes, recent studies show that ZIKV can also be transmitted via sexual contact and cases of sexually transmitted ZIKV have been confirmed in the U.S., France, and Italy. How sexual transmission affects the spread and control of ZIKV infection is not well-understood. We presented a mathematical model to investigate the impact of mosquito-borne and sexual transmission on spread and control of ZIKV and used the model to fit the ZIKV data in Brazil, Colombia, and El Salvador. Based on the estimated parameter values, we calculated the median and confidence interval of the basic reproduction number R0=2.055 (95% CI: 0.523-6.300), in which the distribution of the percentage of contribution by sexual transmission is 3.044 (95% CI: 0.123-45.73). Our study indicates that R0 is most sensitive to the biting rate and mortality rate of mosquitoes while sexual transmission increases the risk of infection and epidemic size and prolongs the outbreak. In order to prevent and control the transmission of ZIKV, it must be treated as not only a mosquito-borne disease but also a sexually transmitted disease

Predicting the size and probability of epidemics in a population with heterogeneous infectiousness and susceptibility

We analytically address disease outbreaks in large, random networks with heterogeneous infectivity and susceptibility. The transmissibility $T_{uv}$ (the probability that infection of $u$ causes infection of $v$) depends on the infectivity of $u$ and the susceptibility of $v$. Initially a single node is infected, following which a large-scale epidemic may or may not occur. We use a generating function approach to study how heterogeneity affects the probability that an epidemic occurs and, if one occurs, its attack rate (the fraction infected). For fixed average transmissibility, we find upper and lower bounds on these. An epidemic is most likely if infectivity is homogeneous and least likely if the variance of infectivity is maximized. Similarly, the attack rate is largest if susceptibility is homogeneous and smallest if the variance is maximized. We further show that heterogeneity in infectious period is important, contrary to assumptions of previous studies. We confirm our theoretical predictions by simulation. Our results have implications for control strategy design and identification of populations at higher risk from an epidemic.Comment: 5 pages, 3 figures. Submitted to Physical Review Letter

Does Glycosylation as a modifier of Original Antigenic Sin explain the case age distribution and unusual toxicity in pandemic novel H1N1 influenza?

Background: A pandemic novel H1N1 swine-origin influenza virus has emerged. Most recently the World Health Organization has announced that in a country-dependent fashion, up to 15% of cases may require hospitalization, often including respiratory support. It is now clear that healthy children and young adults are disproportionately affected, most unusually among those with severe respiratory disease without underlying conditions. One possible explanation for this case age distribution is the doctrine of Original Antigenic Sin, i.e., novel H1N1 may be antigenically similar to H1N1 viruses that circulated at an earlier time. Persons whose first exposure to influenza viruses was to such similar viruses would be relatively immune. However, this principle is not sufficient to explain the graded susceptibility between ages 20 and 60, the reduced susceptibility in children below age 10, and the unusual toxicity observed.Methods: We collected case data from 11 countries, about 60% of all cases reported through mid-July 2009. We compared sequence data for the hemagglutinin of novel H1N1 with sequences of H1N1 viruses from 1918 to the present. We searched for sequence differences that imply loss of antigenicity either directly through amino acid substitution or by the appearance of sites for potential glycosylation proximal to sites known to be antigenic in humans. We also considered T-cell epitopes.Results: In our composite, over 75% of confirmed cases of novel H1N1 occurred in persons ≤ 30 years old, with peak incidence in the age range 10-19 years. Less than 3% of cases occurred in persons over 65, with a gradation in incidence between ages 20 and 60 years.The sequence data indicates that novel H1N1 is most similar to H1N1 viruses that circulated before 1943. Novel H1N1 lacks glycosylation sites on the globular head of hemagglutinin (HA1) near antigenic regions, a pattern shared with the 1918 pandemic strain and H1N1 viruses that circulated until the early 1940s. Later H1N1 viruses progressively added new glycosylation sites likely to shield antigenic epitopes, while T-cell epitopes were relatively unchanged.Conclusions: In this evolutionary context, Original Antigenic Sin exposure should produce an immune response increasingly mismatched to novel H1N1 in progressively younger persons. We suggest that it is this mismatch that produces both the gradation in susceptibility and the unusual toxicity. Several murine studies suggest specific cell types as a likely basis of the unusual toxicity. These studies also point to widely available pharmaceutical agents as plausible candidates for mitigating the toxic effects. The principle of Original Antigenic Sin modified by glycosylation appears to explain both the case age distribution and the unusual toxicity pattern of the novel H1N1 pandemic. In addition, it suggests pharmaceutical agents for immediate investigation for mitigation potential, and provides strategic guidance for the distribution of pandemic mitigation resources of all types. © 2010 Reichert et al; licensee BioMed Central Ltd.published_or_final_versio

Characterizing Ebola Transmission Patterns based on Internet News Reports.

BACKGROUND:  Detailed information on patient exposure, contact patterns, and discharge status, is rarely available in real time from traditional surveillance systems in the context of an emerging infectious disease outbreak. Here we validate the systematic collection of Internet news reports to characterize epidemiological patterns of Ebola virus disease (EVD) infections during the West African 2014-2015 outbreak. METHODS:  Based on 58 news reports, we analyzed a total of 79 EVD clusters (286 cases) of size ranging from 1 to 33 cases between January 2014 and February 2015 in Guinea, Sierra Leone and Liberia. RESULTS AND CONCLUSIONS:  The great majority of reported exposures stemmed from contact with family members (57.3%) followed by hospitals (18.2%) and funerals (12.7%). Our data indicated that funeral exposure was significantly more frequent in Sierra Leone (27.3%) followed by Guinea (18.2%) and Liberia (1.8%) (Chi-square test;

Transmission potential of influenza A/H7N9, February to May 2013, China

Background On 31 March 2013, the first human infections with the novel influenza A/H7N9 virus were reported in Eastern China. The outbreak expanded rapidly in geographic scope and size, with a total of 132 laboratory-confirmed cases reported by 3 June 2013, in 10 Chinese provinces and Taiwan. The incidence of A/H7N9 cases has stalled in recent weeks, presumably as a consequence of live bird market closures in the most heavily affected areas. Here we compare the transmission potential of influenza A/H7N9 with that of other emerging pathogens and evaluate the impact of intervention measures in an effort to guide pandemic preparedness. Methods We used a Bayesian approach combined with a SEIR (Susceptible-Exposed-Infectious-Removed) transmission model fitted to daily case data to assess the reproduction number (R) of A/H7N9 by province and to evaluate the impact of live bird market closures in April and May 2013. Simulation studies helped quantify the performance of our approach in the context of an emerging pathogen, where human-to-human transmission is limited and most cases arise from spillover events. We also used alternative approaches to estimate R based on individual-level information on prior exposure and compared the transmission potential of influenza A/H7N9 with that of other recent zoonoses. Results Estimates of R for the A/H7N9 outbreak were below the epidemic threshold required for sustained human-to-human transmission and remained near 0.1 throughout the study period, with broad 95% credible intervals by the Bayesian method (0.01 to 0.49). The Bayesian estimation approach was dominated by the prior distribution, however, due to relatively little information contained in the case data. We observe a statistically significant deceleration in growth rate after 6 April 2013, which is consistent with a reduction in A/H7N9 transmission associated with the preemptive closure of live bird markets. Although confidence intervals are broad, the estimated transmission potential of A/H7N9 appears lower than that of recent zoonotic threats, including avian influenza A/H5N1, swine influenza H3N2sw and Nipah virus. Conclusion Although uncertainty remains high in R estimates for H7N9 due to limited epidemiological information, all available evidence points to a low transmission potential. Continued monitoring of the transmission potential of A/H7N9 is critical in the coming months as intervention measures may be relaxed and seasonal factors could promote disease transmission in colder months

Scaling laws for the movement of people between locations in a large city

Large scale simulations of the movements of people in a ``virtual'' city and their analyses are used to generate new insights into understanding the dynamic processes that depend on the interactions between people. Models, based on these interactions, can be used in optimizing traffic flow, slowing the spread of infectious diseases or predicting the change in cell phone usage in a disaster. We analyzed cumulative and aggregated data generated from the simulated movements of 1.6 million individuals in a computer (pseudo agent-based) model during a typical day in Portland, Oregon. This city is mapped into a graph with $181,206$ nodes representing physical locations such as buildings. Connecting edges model individual's flow between nodes. Edge weights are constructed from the daily traffic of individuals moving between locations. The number of edges leaving a node (out-degree), the edge weights (out-traffic), and the edge-weights per location (total out-traffic) are fitted well by power law distributions. The power law distributions also fit subgraphs based on work, school, and social/recreational activities. The resulting weighted graph is a ``small world'' and has scaling laws consistent with an underlying hierarchical structure. We also explore the time evolution of the largest connected component and the distribution of the component sizes. We observe a strong linear correlation between the out-degree and total out-traffic distributions and significant levels of clustering. We discuss how these network features can be used to characterize social networks and their relationship to dynamic processes.Comment: 18 pages, 10 figure

Influenza and Pneumonia Mortality in 66 Large Cities in the United States in Years Surrounding the 1918 Pandemic

The 1918 influenza pandemic was a major epidemiological event of the twentieth century resulting in at least twenty million deaths worldwide; however, despite its historical, epidemiological, and biological relevance, it remains poorly understood. Here we examine the relationship between annual pneumonia and influenza death rates in the pre-pandemic (1910–17) and pandemic (1918–20) periods and the scaling of mortality with latitude, longitude and population size, using data from 66 large cities of the United States. The mean pre-pandemic pneumonia death rates were highly associated with pneumonia death rates during the pandemic period (Spearman r = 0.64–0.72; P,0.001). By contrast, there was a weak correlation between pre-pandemic and pandemic influenza mortality rates. Pneumonia mortality rates partially explained influenza mortality rates in 1918 (r = 0.34, P = 0.005) but not during any other year. Pneumonia death counts followed a linear relationship with population size in all study years, suggesting that pneumonia death rates were homogeneous across the range of population sizes studied. By contrast, influenza death counts followed a power law relationship with a scaling exponent of ,0.81 (95%CI: 0.71, 0.91) in 1918, suggesting that smaller cities experienced worst outcomes during the pandemic. A linear relationship was observed for all other years. Our study suggests that mortality associated with the 1918–20 influenza pandemic was in part predetermined by pre-pandemic pneumonia death rates in 66 large US cities, perhaps through the impact of the physical and social structure of each city. Smaller cities suffered a disproportionately high per capita influenza mortality burden than larger ones in 1918, while city size did not affect pneumonia mortality rates in the pre-pandemic and pandemic periods

Characterizing the Epidemiology of the 2009 Influenza A/H1N1 Pandemic in Mexico

Gerardo Chowell and colleagues address whether school closures and other social distancing strategies were successful in reducing pandemic flu transmission in Mexico by analyzing the age- and state-specific incidence of influenza morbidity and mortality in 32 Mexican states