Impact of Public Health Responses During a Measles Outbreak in an Amish Community in Ohio: Modeling the Dynamics of Transmission

Gastañaduy, Paul A.; Funk, Sebastian; Paul, Prabasaj; Tatham, Lilith; Fisher, Nicholas; Budd, Jeremy; Fowler, Brian; Fijter, Sietske de; DiOrio, Mary; Wallace, Gregory S.; Grenfell, Bryan T.

doi:10.1093/aje/kwy082

Cited by 23 publications

(19 citation statements)

References 28 publications

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“…We developed the R package o2geosocial to classify measles cases into transmission clusters and estimate their importation status using routinely collected surveillance data (genotype, age, onset date and location of the cases). As recently observed during the 2018–2019 measles outbreak in New York, delays in childhood vaccination, local susceptibility and increased contacts can lead to large outbreaks following importations [ 52 , 53 ]. Therefore, we were interested in highlighting the effect of imported cases on communities and we focused on short distance transmission to identify areas where they repeatedly caused subsequent transmission chains.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic reconstruction of measles transmission clusters from routinely collected surveillance data

Robert

Kucharski

Gastañaduy

et al. 2020

J. R. Soc. Interface.

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Pockets of susceptibility resulting from spatial or social heterogeneity in vaccine coverage can drive measles outbreaks, as cases imported into such pockets are likely to cause further transmission and lead to large transmission clusters. Characterizing the dynamics of transmission is essential for identifying which individuals and regions might be most at risk. As data from detailed contact-tracing investigations are not available in many settings, we developed an R package called o2geosocial to reconstruct the transmission clusters and the importation status of the cases from their age, location, genotype and onset date. We compared our inferred cluster size distributions to 737 transmission clusters identified through detailed contact-tracing in the USA between 2001 and 2016. We were able to reconstruct the importation status of the cases and found good agreement between the inferred and reference clusters. The results were improved when the contact-tracing investigations were used to set the importation status before running the model. Spatial heterogeneity in vaccine coverage is difficult to measure directly. Our approach was able to highlight areas with potential for local transmission using a minimal number of variables and could be applied to assess the intensity of ongoing transmission in a region.

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Section: Discussionmentioning

confidence: 99%

Probabilistic reconstruction of measles transmission clusters from routinely collected surveillance data

Robert

Kucharski

Gastañaduy

et al. 2020

J. R. Soc. Interface.

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“…For example, models have been used for many diseases to analyze causes of outbreaks and to help optimize response strategies; increasingly, this is happening in real-time during outbreaks [35]. Examples include diphtheria among Rohingya refugees [36], where real-time modeling informed resource allocation and transmission mechanisms; the 2013-16 west African Ebola outbreak [37,38]; optimum vaccination strategies in response to measles outbreaks [39][40][41][42]; and seasonal malaria prophylaxis [43,44]. Recent modeling studies evaluated the impact on dengue of outdoor, truck-mounted ULV spraying in Porto Alegre, Brazil and IRS in Merida, Mexico [10,45].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the deployment of ultra-low volume and targeted indoor residual spraying for dengue outbreak response

et al. 2020

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Recent years have seen rising incidence of dengue and large outbreaks of Zika and chikungunya, which are all caused by viruses transmitted by Aedes aegypti mosquitoes. In most settings, the primary intervention against Aedes-transmitted viruses is vector control, such as indoor, ultra-low volume (ULV) spraying. Targeted indoor residual spraying (TIRS) has the potential to more effectively impact Aedes-borne diseases, but its implementation requires careful planning and evaluation. The optimal time to deploy these interventions and their relative epidemiological effects are, however, not well understood. We used an agentbased model of dengue virus transmission calibrated to data from Iquitos, Peru to assess the epidemiological effects of these interventions under differing strategies for deploying them. Specifically, we compared strategies where spray application was initiated when incidence rose above a threshold based on incidence in recent years to strategies where spraying occurred at the same time(s) each year. In the absence of spraying, the model predicted 361,000 infections [inter-quartile range (IQR): 347,000-383,000] in the period 2000-2010. The ULV strategy with the fewest median infections was spraying twice yearly, in March and October, which led to a median of 172,000 infections [IQR: 158,000-183,000], a 52% reduction from baseline. Compared to spraying once yearly in September, the best thresholdbased strategy utilizing ULV had fewer median infections (254,000 vs. 261,000), but required more spraying (351 vs. 274 days). For TIRS, the best strategy was thresholdbased, which led to the fewest infections of all strategies tested (9,900; [IQR: 8,720-11,400], a 94% reduction), and required fewer days spraying than the equivalent ULV strategy (280). Although spraying twice each year is likely to avert the most infections, our results PLOS COMPUTATIONAL BIOLOGY

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“…For example, models have been used for many diseases to analyze causes of outbreaks and to help optimize response strategies; increasingly, this is happening in realtime during outbreaks [33]. Examples include diphtheria among Rohingya refugees [34], where realtime modeling informed resource allocation and transmission mechanisms; the 2013-16 west African Ebola outbreak [35,36]; optimum vaccination strategies in response to measles outbreaks [37][38][39][40]; and seasonal malaria prophylaxis [41,42]. Recent modeling studies evaluated the impact on dengue of outdoor, truck-mounted ULV spraying in Porto Alegre, Brazil and IRS in Merida, Mexico [8,43].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the deployment of ultra-low volume and indoor residual spraying for dengue outbreak response

Cavany

España

Lloyd

et al. 2019

Preprint

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Recent years have seen rising incidence of dengue and large outbreaks of Zika and chikungunya, which are all caused by viruses transmitted by Aedes aegypti mosquitoes. In most settings, the primary intervention against Aedes-transmitted viruses is vector control, such as indoor, ultra-low volume (ULV) spraying. Targeted indoor residual spraying (TIRS) has the potential to more effectively impact Aedes-borne diseases, but its implementation requires careful planning and evaluation. The optimal time to deploy these interventions and their relative epidemiological effects are not well understood, however. We used an agent-based model of dengue virus transmission calibrated to data from Iquitos, Peru to assess the epidemiological effects of these interventions under differing strategies for deploying them. Specifically, we compared strategies where spray application was initiated when incidence rose above a threshold based on incidence in recent years to strategies where spraying occurred at the same time(s) each year. In the absence of spraying, the model predicted 361,000 infections [inter-quartile range (IQR): 347,000 - 383,000] in the period 2000-2010. The ULV strategy with the fewest median infections was spraying twice yearly, in March and October, which led to a median of 172,000 infections [IQR: 158,000 - 183,000] over the 11-year study period, a 52% reduction from baseline. Compared to spraying once yearly in September, the best threshold-based strategy utilizing ULV had fewer median infections (254,000 vs. 261,000), but required more spraying (351 vs. 274 days). For TIRS, the best strategy was threshold-based, which led to the fewest infections of all strategies tested (9,900; [IQR: 8,720 - 11,400], a 94% reduction), and required fewer days spraying than the equivalent ULV strategy (280). Although spraying twice each year is likely to avert the most infections, our results indicate that a threshold-based strategy can become an alternative to better balance the translation of spraying effort into impact, particularly if used with a residual insecticide.

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Impact of Public Health Responses During a Measles Outbreak in an Amish Community in Ohio: Modeling the Dynamics of Transmission

Cited by 23 publications

References 28 publications

Probabilistic reconstruction of measles transmission clusters from routinely collected surveillance data

Probabilistic reconstruction of measles transmission clusters from routinely collected surveillance data

Optimizing the deployment of ultra-low volume and targeted indoor residual spraying for dengue outbreak response

Optimizing the deployment of ultra-low volume and indoor residual spraying for dengue outbreak response

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