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, 9 (2), e90094
eCollection

Using Mathematical Transmission Modelling to Investigate Drivers of Respiratory Syncytial Virus Seasonality in Children in the Philippines

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Using Mathematical Transmission Modelling to Investigate Drivers of Respiratory Syncytial Virus Seasonality in Children in the Philippines

Stuart Paynter et al. PLoS One.

Abstract

We used a mathematical transmission model to estimate when ecological drivers of respiratory syncytial virus (RSV) transmissibility would need to act in order to produce the observed seasonality of RSV in the Philippines. We estimated that a seasonal peak in transmissibility would need to occur approximately 51 days prior to the observed peak in RSV cases (range 49 to 67 days). We then compared this estimated seasonal pattern of transmissibility to the seasonal patterns of possible ecological drivers of transmissibility: rainfall, humidity and temperature patterns, nutritional status, and school holidays. The timing of the seasonal patterns of nutritional status and rainfall were both consistent with the estimated seasonal pattern of transmissibility and these are both plausible drivers of the seasonality of RSV in this setting.

Conflict of interest statement

Competing Interests: Sanofi Pasteur contributed towards direct research costs related to data management for the PCV trial. The authors do not believe this constitutes a conflict of interest related to this manuscript. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. RSV model schematic.
S1 are susceptible individuals before their first RSV infection. E1 are individuals infected for the first time but not yet infectious. I1 are individuals infected for the first time and now infectious. R are individuals recovered from infection and temporarily resistant to reinfection. S2 are partially susceptible individuals before later RSV infections. E2 are individuals with subsequent infections but not yet infectious. I2 are individuals with subsequent infections and now infectious.
Figure 2
Figure 2. Cumulative incidence of first RSV infection according to age.
Mean RSV incidence from birth cohorts in Kilifi and Houston (lines) compared to results from anti-RSV IgG seroprevalence surveys (data points with 95% CIs).
Figure 3
Figure 3. Results of the RSV model.
RSV case estimates derived from the model were fitted to the observed number of RSV cases. Mean λ  = 0.0022 per day. Data from Bohol, the Philippines, 2001 to 2004.
Figure 4
Figure 4. Timing of the estimated seasonal variation in the transmission coefficient (β) relative to observed seasonal exposures.
The red line shows the estimated variation in β. The black lines show the variation in the observed exposures. Data from Bohol, the Philippines, 2001 to 2004.

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Publication types

Grant support

The RSV admissions data and weight for age data were collected as part of the ARIVAC project, which was supported by the European Commission DG Research INCO program (contracts IC18-CY97-2019, ICA4-CT-1999-10008, ICA4-CT-2002-10062); Academy of Finland (contracts: 206283, 106974, 108873, and 108878); Finnish Ministry of Foreign Affairs (bilateral contracts 75502901 and 327/412/2000); Finnish Physicians for Social Responsibility; GAVI ADIP Pneumo; Sanofi Pasteur; Research Institute for Tropical Medicine of the Philippines; National Public Health Institute Finland; University of Queensland; University of Colorado; National Health and Medical Research Council of Australia; and Programme for Appropriate Technology in Health (PATH). The views expressed by the authors do not necessarily reflect the views of PATH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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