Community factors associated with local epidemic timing of respiratory syncytial virus: A spatiotemporal modeling study

Respiratory syncytial virus (RSV) causes a large burden of morbidity in young children and the elderly. Spatial variability in the timing of RSV epidemics provides an opportunity to probe the factors driving its transmission, including factors that influence epidemic seeding and growth rates. Using hospitalization data from Connecticut, New Jersey, and New York, we estimated epidemic timing at the ZIP code level using harmonic regression and then used a Bayesian meta-regression model to evaluate correlates of epidemic timing. Earlier epidemics were associated with larger household size and greater population density. Nearby localities had similar epidemic timing. Our results suggest that RSV epidemics grow faster in areas with more local contact opportunities, and that epidemic spread follows a spatial diffusion process based on geographic proximity. Our findings can inform the timing of delivery of RSV extended half-life prophylaxis and maternal vaccines and guide future studies on the transmission dynamics of RSV.

Community factors associated with local RSV epidemic patterns: a spatiotemporal modeling study

Background: Respiratory syncytial virus (RSV) causes a large burden of morbidity in infants, young children, and the elderly. The timing of RSV seasonal epidemics exhibits strong spatial patterns within the United States. Spatial variability in the timing of RSV epidemics provides an opportunity to probe the factors driving transmission of the virus. Methods: We evaluated competing hypotheses about the associations between RSV epidemic timing at the ZIP-code level and household size, population density, school district boundaries, commuting patterns, and geographic proximity. We used hierarchical Bayesian models with monthly ZIP-code level hospitalization data from New York, New Jersey, and Connecticut between July 1997 and June 2014 to investigate these hypotheses. Results: Early epidemic timing across ZIP codes was associated with large household sizes and high population density, and nearby ZIP codes shared similar epidemic timing. Variations in epidemic timing corresponding to commuting patterns or school district boundaries were negligible. Conclusion: Our results suggest RSV epidemics take off faster in areas with more household crowding and higher population density, and that epidemic spread follows a spatial diffusion process based on geographic proximity. With several vaccines against RSV under development, it is important to understand the drivers of RSV transmission and disease in order to maximize population protection of a vaccine program. Our findings can inform more effective control measures against RSV, such as vaccine programs and household infection control, and guide future studies on the transmission dynamics of RSV.

Comparative Seasonal Respiratory Virus Epidemic Timing in Utah

Previous studies have found evidence of viral interference between seasonal respiratory viruses. Using laboratory-confirmed data from a Utah-based healthcare provider, Intermountain Health Care, we analyzed the time-specific patterns of respiratory syncytial virus (RSV), influenza A, influenza B, human metapneumovirus, rhinovirus, and enterovirus circulation from 2004 to 2018, using descriptive methods and wavelet analysis (n = 89,462) on a local level. The results showed that RSV virus dynamics in Utah were the most consistent of any of the viruses studied, and that the other seasonal viruses were generally in synchrony with RSV, except for enterovirus (which mostly occurs late summer to early fall) and influenza A and B during pandemic years.

Demographic buffering: titrating the effects of birth rate and imperfect immunity on epidemic dynamics

Host demography can alter the dynamics of infectious disease. In the case of perfectly immunizing infections, observations of strong sensitivity to demographic variation have been mechanistically explained through analysis of the susceptible–infected–recovered (SIR) model that assumes lifelong immunity following recovery from infection. When imperfect immunity is incorporated into this framework via the susceptible–infected–recovered–susceptible (SIRS) model, with individuals regaining full susceptibility following recovery, we show that rapid loss of immunity is predicted to buffer populations against the effects of demographic change. However, this buffering is contrary to the dependence on demography recently observed for partially immunizing infections such as rotavirus and respiratory syncytial virus. We show that this discrepancy arises from a key simplification embedded in the SIR(S) framework, namely that the potential for differential immune responses to repeat exposures is ignored. We explore the minimum additional immunological information that must be included to reflect the range of observed dependencies on demography. We show that including partial protection and lower transmission following primary infection is sufficient to capture more realistic reduced levels of buffering, in addition to changes in epidemic timing, across a range of partially and fully immunizing infections. Furthermore, our results identify key variables in this relationship, including R 0 .

Effect of environmental factors on the spatio-temporal patterns of influenza spread

SUMMARYAlthough spatio-temporal patterns of influenza spread often suggest that environmental factors play a role, their effect on the geographical variation in the timing of annual epidemics has not been assessed. We examined the effect of solar radiation, dew point, temperature and geographical position on the city-specific timing of epidemics in the USA. Using paediatric in-patient data from hospitals in 35 cities for each influenza season in the study period 2000–2005, we determined ‘epidemic timing’ by identifying the week of peak influenza activity. For each city we calculated averages of daily climate measurements for 1 October to 31 December. Bayesian hierarchical models were used to assess the strength of association between each variable and epidemic timing. Of the climate variables only solar radiation was significantly related to epidemic timing (95% CI −0·027 to −0·0032). Future studies may elucidate biological mechanisms intrinsically linked to solar radiation that contribute to epidemic timing in temperate regions.