Absolute Humidity and the Seasonal Onset of Influenza in the Continental United States

Here, the authors demonstrate that variations of absolute humidity explain both the onset of wintertime influenza transmission and the overarching seasonality of this pathogen in temperate regions

Impact of Temperature Relative Humidity and Absolute Humidity on the Incidence of Hospitalizations for Lower Respiratory Tract Infections Due to Influenza, Rhinovirus, and Respiratory Syncytial Virus: Results from Community-Acquired Pneumonia Organization (CAPO) International Cohort Study

Abstract Background: Transmissibility of several etiologies of lower respiratory tract infections (LRTI) may vary based on outdoor climate factors. The objective of this study was to evaluate the impact of outdoor temperature, relative humidity, and absolute humidity on the incidence of hospitalizations for lower respiratory tract infections due to influenza, rhinovirus, and respiratory syncytial virus (RSV). Methods: This was a secondary analysis of an ancillary study of the Community Acquired Pneumonia Organization (CAPO) database. Respiratory viruses were detected using the Luminex xTAG respiratory viral panel. Climate factors were obtained from the National Weather Service. Adjusted Poisson regression models with robust error variance were used to model the incidence of hospitalization with a LRTI due to: 1) influenza, 2) rhinovirus, and 3) RSV (A and/or B), separately. Results: A total of 467 hospitalized patients with LRTI were included in the study; 135 (29%) with influenza, 41 (9%) with rhinovirus, and 27 (6%) with RSV (20 RSV A, 7 RSV B). The average, minimum, and maximum absolute humidity and temperatur e variables were associated with hospitalization due to influenza LRTI, while the relative humidity variables were not. None of the climate variables were associated with hospitalization due to rhinovirus or RSV. Conclusions: This study suggests that outdoor absolute humidity and temperature are associated with hospitalizations due to influenza LRTIs, but not with LRTIs due to rhinovirus or RSV. Understanding factors contributing to the transmission of respiratory viruses may assist in the prediction of future outbreaks and facilitate the development of transmission prevention interventions

A Study on Physical Factors that may Influence the Spread and Occurrence of Influenza

Influenza is one of respiratory infections causing the highest morbidity and mortality rates. Every day tens of millions of people suffer from virus infection worldwide with varying severity and with a high economic impact. Influenza transmission from person to person occurs in various ways. This study is an attempt to understand airborne transmission in indoor locations by examining the relation between environmental parameters such as temperature and relative humidity with the number of influenza like illness (ILI) cases. It is proposed that the pattern of influenza activity is primarily a function of indoor relative humidity in temperate regions. This conclusion is based on previous virus viability experiments and on our observation of a strong correlation between influenza like illness cases and indoor relative humidity. Historical data reveals that the peak in influenza like illness cases occurs when the indoor relative humidity is around 10-30%. This study also focuses on the aerosol mode of transmission via expelled particles of human cough. Experiments were carried out for concentration measurements at various locations of cough particles in an Environmental Chamber (EC) at a Morgantown NIOSH facility. A simulated cough machine capable of replicating human cough in real time flow and particle size distribution was used for the aerosol injection. A computational fluid dynamics (CFD) model was developed to simulate the human cough in a modeled room. The results of experiments and simulations are compared to assess the suitability and accuracy of CFD simulation for such flow. The last step in this study is to evaluate the potential of inhalation of dispersed cough droplets in room by breathing. Since the primary mechanism of infection transmission is believed to be via inhalation of virus laden droplets, a theoretical study was conducted to define the sphere of influence of human breathing

Probabilistic Daily ILI Syndromic Surveillance with a Spatio-Temporal Bayesian Hierarchical Model

BACKGROUND: For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending epidemic. This detection or alert surely contains uncertainty, and thus should be evaluated with a proper probabilistic measure. However, traditional monitoring mechanisms simply provide a binary alert, failing to adequately address this uncertainty. METHODS AND FINDINGS: Based on the Bayesian posterior probability of influenza-like illness (ILI) visits, the intensity of outbreak can be directly assessed. The numbers of daily emergency room ILI visits at five community hospitals in Taipei City during 2006-2007 were collected and fitted with a Bayesian hierarchical model containing meteorological factors such as temperature and vapor pressure, spatial interaction with conditional autoregressive structure, weekend and holiday effects, seasonality factors, and previous ILI visits. The proposed algorithm recommends an alert for action if the posterior probability is larger than 70%. External data from January to February of 2008 were retained for validation. The decision rule detects successfully the peak in the validation period. When comparing the posterior probability evaluation with the modified Cusum method, results show that the proposed method is able to detect the signals 1-2 days prior to the rise of ILI visits. CONCLUSIONS: This Bayesian hierarchical model not only constitutes a dynamic surveillance system but also constructs a stochastic evaluation of the need to call for alert. The monitoring mechanism provides earlier detection as well as a complementary tool for current surveillance programs

Geographic and demographic transmission patterns of the 2009 A/H1N1 influenza pandemic in the United States

This thesis describes how transmission of the 2009 A/H1N1 influenza pandemic in the United States varied geographically, with emphasis on population distribution and age structure. This is made possible by the availability of medical claims records maintained in the private sector that capture the weekly incidence of influenza-like illness in 834 US cities. First, a probabilistic method is developed to infer each city's outbreak onset time. This reveals a clear wave-like pattern of transmission originating in the south-eastern US. Then, a mechanistic mathematical model is constructed to describe the between-city transmission of the epidemic. A model selection procedure reveals that transmission to a city is modulated by its population size, surrounding population density, and possibly by students mixing in schools. Geographic variation in transmissibility is explored further by nesting a latent Gaussian process within the mechanistic transmission model, revealing a possible region of elevated transmissibility in the south-eastern US. Then, using the mechanistic model and a probabilistic back-tracing procedure, the geographic introduction sites (the `transmission hubs') of the outbreak are identified. The transmission hubs of the 2009 pandemic were generally mid-sized cities, contrasting with the conventional perspective that major outbreaks should start in large population centres with high international connectivity. Transmission is traced forward from these hubs to identify `basins of infection', or regions where outbreaks can be attributed with high probability to a particular hub. The city-level influenza data is also separated into 12 age categories. Techniques adapted from signal processing reveal that school-aged children may have been key drivers of the epidemic. Finally, to provide a point of comparison, the procedures described above are applied to the 2003-04 and 2007-08 seasonal influenza outbreaks. Since the 2007-08 outbreak featured three antigenically distinct strains of influenza, it is possible to identify which antigenic strains may have been responsible for infecting each transmission hub. These strains are identified using a probabilistic model that is joined with the geographic transmission model, providing a link between population dynamics and molecular surveillance.Gates Cambridge scholarshi

International Externalities in Pandemic Influenza Mitigation

A serious influenza pandemic could be devastating for the world. Ideally, such a pandemic could be contained, but this may be infeasible. One promising method for pandemic mitigation is to treat infectious individuals with antiviral pharmaceuticals. While most of the benefits from treatment accrue to the country in which treatment occurs, there are some positive spillovers: when one country treats more of its population this both reduces the attack rate in the other country and increases the marginal benefit from additional treatment in the other country. These externalities and complementarities may mean that self-interested rich countries should optimally pay for some AV treatment in poor countries. This dissertation demonstrates the presence of antiviral treatment externalities in simple epidemiological SIR models, and then in a descriptively realistic Global Epidemiological Model (GEM). This GEM simulates pandemic spread between cities through the international airline network, and between cities and rural areas through ground transport. Under the base case assumptions of moderate transmissibility of the flu, the distribution of antiviral stockpiles from rich countries to poor and lower middle income countries may indeed pay for itself: providing a stockpile equal to 1% of the population of poor countries will reduce cases in rich countries after 1 year by about 6.13 million cases at a cost of 4.62 doses per rich-country case avoided. Concentrating doses on the outbreak country is, however, even more cost-effective: in the base case it reduces the number of influenza cases by 4.76 million cases, at the cost of roughly 1.92 doses per case avoided. These results depend on the transmissibility of the flu strain, the efficacy of antivirals in reducing infection and on the proportion of infectious who can realistically be identified and treated

A Mathematical Framework for Estimating Risk of Airborne Transmission of COVID-19 with Application to Face Mask Use and Social Distancing

A mathematical model for estimating the risk of airborne transmission of a respiratory infection such as COVID-19, is presented. The model employs basic concepts from fluid dynamics and incorporates the known scope of factors involved in the airborne transmission of such diseases. Simplicity in the mathematical form of the model is by design, so that it can serve not only as a common basis for scientific inquiry across disciplinary boundaries, but also be understandable by a broad audience outside science and academia. The caveats and limitations of the model are discussed in detail. The model is used to assess the protection from transmission afforded by face coverings made from a variety of fabrics. The reduction in transmission risk associated with increased physical distance between the host and susceptible is also quantified by coupling the model with available data on scalar dispersion in canonical flows. Finally, the effect of the level of physical activity (or exercise intensity) of the host and the susceptible in enhancing transmission risk, is also assessed

Avoiding the Great Filter: A Simulation of Important Factors for Human Survival

Humanity's path to avoiding extinction is a daunting and inevitable challenge which proves difficult to solve, partially due to the lack of data and evidence surrounding the concept. We aim to address this confusion by addressing the most dangerous threats to humanity, in hopes of providing a direction to approach this problem. Using a probabilistic model, we observed the effects of nuclear war, climate change, asteroid impacts, artificial intelligence and pandemics, which are the most harmful disasters in terms of their extent of destruction on the length of human survival. We consider the starting point of the predicted average number of survival years as the present calendar year. Nuclear war, when sampling from an artificial normal distribution, results in an average human survival time of 60 years into the future starting from the present, before a civilization-ending disaster. While climate change results in an average human survival time of 193 years, the simulation based on impact from asteroids results in an average of 1754 years. Since the risks from asteroid impacts could be considered to reside mostly in the far future, it can be concluded that nuclear war, climate change, and pandemics are presently the most prominent threats to humanity. Additionally, the danger from superiority of artificial intelligence over humans, although still somewhat abstract, is worthy of further study as its potential for impeding humankind's progress towards becoming a more advanced civilization cannot be confidently dismissed

Absolute humidity and the seasonal onset of influenza in the continental United States

Corrections 18 Mar 2010: Shaman J, Pitzer VE, Viboud C, Grenfell BT, Lipsitch M (2010) Correction: Absolute Humidity and the Seasonal Onset of Influenza in the Continental United States. PLOS Biology 8(3): 10.1371/annotation/35686514-b7a9-4f65-9663-7baefc0d63c0. 15 Mar 2010: Shaman J, Pitzer VE, Viboud C, Grenfell BT, Lipsitch M (2010) Correction: Absolute Humidity and the Seasonal Onset of Influenza in the Continental United States. PLOS Biology 8(3): 10.1371/annotation/9ddc5251-72a1-4eba-ae35-9ab04488527b.Much of the observed wintertime increase of mortality in temperate regions is attributed to seasonal influenza. A recent reanalysis of laboratory experiments indicates that absolute humidity strongly modulates the airborne survival and transmission of the influenza virus. Here, we extend these findings to the human population level, showing that the onset of increased wintertime influenza-related mortality in the United States is associated with anomalously low absolute humidity levels during the prior weeks. We then use an epidemiological model, in which observed absolute humidity conditions temper influenza transmission rates, to successfully simulate the seasonal cycle of observed influenza-related mortality. The model results indicate that direct modulation of influenza transmissibility by absolute humidity alone is sufficient to produce this observed seasonality. These findings provide epidemiological support for the hypothesis that absolute humidity drives seasonal variations of influenza transmission in temperate regions