Pandemic influenza A (H1N1) 2009 (pandemic H1N1) is spreading throughout the planet. It has become the dominant strain in the southern hemisphere, where the influenza season is underway. Here, based on reported case clusters in the USA, we estimate the household secondary attack rate for pandemic H1N1 to be 27.3% (95% CI: 12.2%-50.5%). From a school outbreak, we estimate a school child infects 2.4 (95% CI: 1.8-3.2) other children within the school. We estimate the basic reproductive number, R 0 , to range from 1.3-1.7 and the generation interval to range from 2.6-3.2 days. We use a simulation model to evaluate the effectiveness of vaccination strategies in the USA for the Fall, 2009. If vaccine were available soon enough, vaccination of children, followed by adults, reaching 70% overall coverage, in addition to high risk and essential workforce groups, could mitigate a severe epidemic.Pandemic H1N1, which first emerged in Mexico in , spread worldwide, resulting in more than 130,000 laboratory-confirmed cases and 800 deaths in over 100 countries by midJuly (1). The global distribution of this novel strain prompted the World Health Organization to declare the first influenza pandemic of the 21st century in June 2009 (2). Initially, most cases were clustered in households (3-6) and schools (7) with over 50% of the reported cases in school children in the 5-18 year old age range. A recent analysis of data from the United States, Canada, the United Kingdom, and the European Union suggests case fatality ratios ranging from 0.20%-0.68% in these regions and a higher case fatality ratio in Mexico of 1.23% (95% CI 1.03%-1.47%) (8).Both pandemic and seasonal influenza cause sustained epidemics in the upper northern hemisphere (above latitude ~ 20°N) and lower southern hemisphere (below latitude ~ 20°S) during the respective late Fall to early Spring months, with epidemics in the more tropical regions (between latitudes ~ 20°S and 20°N) occurring sporadically, but sometimes corresponding to the rainy season. The last influenza pandemic was the Hong Kong A (H3N2) * To whom correspondence should be addressed. longini@scharp.org . 1957 -1958 , caused mid-Summer, 1957, outbreaks in Louisiana schools that were open in the Summer because of the need for children helping with the Spring harvest (11). However, there was no extensive community-wide spread of influenza A (H2N2) in the USA until the Fall of 1957, with the national level epidemic rising in September and peaking in October. Pandemic H1N1 will probably spread in a similar spatio-temporal pattern as previous pandemics, but accelerated due to increased air travel (12). Supporting Online MaterialEstimates of the transmissibility of pandemic H1N1are crucial to devising effective mitigation strategies. Historically, the best characterization of influenza transmissibility has been based on the household secondary attack rate. The household secondary attack rate is the probability (sometimes expressed as a percent) that an infected person in the household will infect a...

Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development.

Background: The 2014 West African Ebola Outbreak is so far the largest and deadliest recorded in history. The affected countries, Sierra Leone, Guinea, Liberia, and Nigeria, have been struggling to contain and to mitigate the outbreak. The ongoing rise in confirmed and suspected cases, 2615 as of 20 August 2014, is considered to increase the risk of international dissemination, especially because the epidemic is now affecting cities with major commercial airports. Method: We use the Global Epidemic and Mobility Model to generate stochastic, individual based simulations of epidemic spread worldwide, yielding, among other measures, the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. The mobility model integrates daily airline passenger traffic worldwide and the disease model includes the community, hospital, and burial transmission dynamic. We use a multimodel inference approach calibrated on data from 6 July to the date of 9 August 2014. The estimates obtained were used to generate a 3-month ensemble forecast that provides quantitative estimates of the local transmission of Ebola virus disease in West Africa and the probability of international spread if the containment measures are not successful at curtailing the outbreak. Results: We model the short-term growth rate of the disease in the affected West African countries and estimate the basic reproductive number to be in the range 1.5 − 2.0 (interval at the 1/10 relative likelihood). We simulated the international spreading of the outbreak and provide the estimate for the probability of Ebola virus disease case importation in countries across the world. Results indicate that the short-term (3 and 6 weeks) probability of international spread outside the African region is small, but not negligible. The extension of the outbreak is more likely occurring in African countries, increasing the risk of international dissemination on a longer time scale.

Summary Background The 2014 Ebola epidemic in West Africa defines an unprecedented health threat. We developed a model of Ebola transmission that integrates detailed geographical and demographic data from Liberia to overcome the limitations of non-spatial approaches in projecting the disease dynamics and assessing non-pharmaceutical control interventions. Methods We use a spatial agent-based model calibrated using a Markov chain Monte Carlo approach. The model is used to estimate Ebola transmission parameters and investigate the effectiveness of interventions such as availability of Ebola Treatment Units, safe burials procedures and household protection kits. Findings Through August 16, 2014, we estimate that 38·3% (95%CI 17·4-76·4) of infections were acquired in hospitals, 30·7% (95%CI 14·1-46·4) in households, and 8·9% (95%CI 3·3-11·8) while participating in funerals. The movement and mixing of Ebola and non-Ebola patients in hospitals at the early stage of the epidemic is found to be a sufficient driver of the observed pattern of spatial spread. The subsequent decrease of incidence at country and county level is ascribable to the increasing availability of Ebola treatment units – which in turn contributed to drastically decrease hospital transmission – safe burials, and distribution of household protection kits. Interpretation The model allows evaluating intervention options and disentangling their role in the decrease of incidence observed since September 7, 2014. High-quality data - e.g. to estimate household secondary attack rate, contact patterns within hospitals, and effects of ongoing interventions - are needed to reduce uncertainty in model estimates.

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.

In October 2010, a virulent South Asian strain of El Tor cholera began to spread in Haiti. Interventions have included treatment of cases and improved sanitation. Use of cholera vaccines would likely have further reduced morbidity and mortality, but such vaccines are in short supply and little is known about effective vaccination strategies for epidemic cholera. We use a mathematical cholera transmission model to assess different vaccination strategies. With limited vaccine quantities, concentrating vaccine in high-risk areas is always most efficient. We show that targeting one million doses of vaccine to areas with high exposure to Vibrio cholerae, enough for two doses for 5% of the population, would reduce the number of cases by 11%. The same strategy with enough vaccine for 30% of the population with modest hygienic improvement could reduce cases by 55% and save 3,320 lives. For epidemic cholera, we recommend a large mobile stockpile of enough vaccine to cover 30% of a country's population to be reactively targeted to populations at high risk of exposure.infectious diseases | simulation modeling

Vaccinating school-aged children against influenza can reduce age-specific and population-level illness attack rates. Using a stochastic simulation model of influenza transmission, the authors assessed strategies for vaccinating children in the United States, varying the vaccine type, coverage level, and reproductive number R (average number of secondary cases produced by a typical primary case). Results indicated that vaccinating children can substantially reduce population-level illness attack rates over a wide range of scenarios. The greatest absolute reduction in influenza illness cases per season occurred at R values ranging from 1.2 to 1.6 for a given vaccine coverage level. The indirect, total, and overall effects of vaccinating children were strong when transmission intensity was low to intermediate. The indirect effects declined rapidly as transmission intensity increased. In a mild influenza season (R = 1.1), approximately 19 million influenza cases could be prevented by vaccinating 70% of children. At most, nearly 100 million cases of influenza illness could be prevented, depending on the proportion of children vaccinated and the transmission intensity. Given the current worldwide threat of novel influenza A (H1N1), with an estimated R of 1.4–1.6, health officials should consider strategies for vaccinating children against novel influenza A (H1N1) as well as seasonal influenza.

International audienceThe quick spread of an Ebola outbreak in West Africa has led a number of countries and airline companies to issue travel bans to the affected areas. Considering data up to 31 Aug 2014, we assess the impact of the resulting traffic reductions with detailed numerical simulations of the international spread of the epidemic. Traffic reductions are shown to delay by only a few weeks the risk that the outbreak extends to new countries