Spatial modes for transmission of chikungunya virus during a large chikungunya outbreak in Italy: a modeling analysis

Guzzetta, Giorgio; Vairo, Francesco; Mammone, Alessia; Lanini, Simone; Poletti, Piero; Manica, Mattia; Rosà, Roberto; Caputo, Beniamino; Solimini, Angelo G.; Torre, Alessandra della; Scognamiglio, Paola; Zumla, Alimuddin; Ippolito, Giuseppe; Merler, Stefano

doi:10.1186/s12916-020-01674-y

Cited by 23 publications

(15 citation statements)

References 28 publications

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“…By targeting the Culex species at their peak activity time, their populations would need approximately 50 days to rebound to the levels prior to the adulticide spraying in contrast with no reduction if the intervention would have been done just a few hours earlier. From an epidemiological standpoint, it is important to note that a 90% reduction in the vector population over a time span of 5 days (i.e., less than a generation time of Zika, dengue, and chikungunya [ 41 – 43 ]) would have a substantial effect on the control of an arbovirus outbreak, causing reproduction numbers of up to 10 to fall below the epidemic threshold of 1. In fact, the reproduction number (i.e., the number of secondary infections generated by an infective host through vector bites in a fully susceptible host population) for a mosquito-borne infectious disease is nearly directly proportional to the number of adult female vectors per human host.…”

Section: Discussionmentioning

confidence: 99%

Diel activity patterns of vector mosquito species in the urban environment: Implications for vector control strategies

Wilke

Mhlanga²,

Kummer³

et al. 2023

PLoS Negl Trop Dis

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Mathematical models have been widely used to study the population dynamics of mosquitoes as well as to test and validate the effectiveness of arbovirus outbreak responses and mosquito control strategies. The objective of this study is to assess the diel activity of mosquitoes in Miami-Dade, Florida, and Brownsville, Texas, the most affected areas during the Zika outbreak in 2016–2017, and to evaluate the effectiveness of simulated adulticide treatments on local mosquito populations. To assess variations in the diel activity patterns, mosquitoes were collected hourly for 96 hours once a month from May through November 2019 in Miami-Dade County, Florida, and Brownsville, Texas. We then performed a PERMANOVA followed by a SIMPER analysis to assess whether the abundance and species richness significantly varies at different hours of the day. Finally, we used a mathematical model to simulate the population dynamics of 5 mosquito vector species and evaluate the effectiveness of the simulated adulticide applications. A total of 14,502 mosquitoes comprising 17 species were collected in Brownsville and 10,948 mosquitoes comprising 19 species were collected in Miami-Dade County. Aedes aegypti was the most common mosquito species collected every hour in both cities and peaking in abundance in the morning and the evening. Our modeling results indicate that the effectiveness of adulticide applications varied greatly depending on the hour of the treatment. In both study locations, 9 PM was the best time for adulticide applications targeting all mosquito vector species; mornings/afternoons (9 AM– 5 PM) yielded low effectiveness, especially for Culex species, while at night (12 AM– 6 AM) the effectiveness was particularly low for Aedes species. Our results indicate that the timing of adulticide spraying interventions should be carefully considered by local authorities based on the ecology of the target mosquito species in the focus area.

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Section: Discussionmentioning

confidence: 99%

Diel activity patterns of vector mosquito species in the urban environment: Implications for vector control strategies

Wilke

Mhlanga²,

Kummer³

et al. 2023

PLoS Negl Trop Dis

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“…Therefore, dates of infection were imputed multiple times based on the distribution of the incubation period [32]. The same intrinsic limitation of the unobservability of infection times is shared by all transmission chain reconstruction models, but there are now several examples where these models have been proven to correctly identify the transmission dynamics of infectious outbreaks [32][33][34][35][36]. Estimates were substantially robust with respect to different methods of imputation and different distributions of the incubation period (Fig.…”

Section: Discussionmentioning

confidence: 99%

Estimation of the incubation period and generation time of SARS-CoV-2 Alpha and Delta variants from contact tracing data

et al. 2022

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Quantitative information on epidemiological quantities such as the incubation period and generation time of SARS-CoV-2 variants is scarce. We analyzed a dataset collected during contact tracing activities in the province of Reggio Emilia, Italy, throughout 2021. We determined the distributions of the incubation period for the Alpha and Delta variants using information on negative PCR tests and the date of last exposure from 282 symptomatic cases. We estimated the distributions of the intrinsic generation time using a Bayesian inference approach applied to 9724 SARS-CoV-2 cases clustered in 3545 households where at least one secondary case was recorded. We estimated a mean incubation period of 4.9 days (95% credible intervals, CrI, 4.4-5.4) for Alpha and 4.5 days (95%CrI 4.0-5.0) for Delta. The intrinsic generation time was estimated to have a mean of 7.12 days (95% CrI 6.27-8.44) for Alpha and of 6.52 days (95%CrI 5.54-8.43) for Delta. The household serial interval was 2.43 days (95%CrI 2.29-2.58) for Alpha and 2.74 days (95%CrI 2.62-2.88) for Delta, and the estimated proportion of pre-symptomatic transmission was 48-51% for both variants. These results indicate limited differences in the incubation period and intrinsic generation time of SARS-CoV-2 variants Alpha and Delta compared to ancestral lineages.

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“…On one hand, this prevented us to apply standard Bayesian approaches to reconstructing the occurred transmission chains. 9 , 21 On the other hand, when an infection episode was identified between individuals involved in the scholastic settings, we cannot exclude that the transmission occurred outside school. As for other epidemiological investigations, it is likely that some contacts were not identified or remained untested.…”

Section: Discussionmentioning

confidence: 99%

Estimating SARS‐CoV‐2 transmission in educational settings: A retrospective cohort study

Manica

Poletti

Deandrea

et al. 2022

Influenza Resp Viruses

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Background School closures and distance learning have been extensively adopted to counter the COVID‐19 pandemic. However, the contribution of school transmission to the spread of SARS‐CoV‐2 remains poorly quantified. Methods We analyzed transmission patterns associated with 976 SARS‐CoV‐2 exposure events, involving 460 positive individuals, as identified in early 2021 through routine surveillance and an extensive screening conducted on students, school personnel, and their household members in a small Italian municipality. In addition to population screenings and contact‐tracing operations, reactive closures of class and schools were implemented. Results From the analysis of 152 clear infection episodes and 584 exposure events identified by epidemiological investigations, we estimated that approximately 50%, 21%, and 29% of SARS‐CoV‐2 transmission was associated with household, school, and community contacts, respectively. We found substantial transmission heterogeneities, with 20% positive individuals causing 75% to 80% of ascertained infection episodes. A higher proportion of infected individuals causing onward transmission was found among students (46.2% vs. 25%, on average), who also caused a markedly higher number of secondary cases (mean: 1.03 vs. 0.35). By reconstructing likely transmission chains from the entire set of exposures identified during contact‐tracing operations, we found that clusters originated from students or school personnel were associated with a larger average cluster size (3.32 vs. 1.15) and a larger average number of generations in the transmission chain (1.56 vs. 1.17). Conclusions Uncontrolled SARS‐CoV‐2 transmission at school could disrupt the regular conduct of teaching activities, likely seeding the transmission into other settings, and increasing the burden on contact‐tracing operations.

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Spatial modes for transmission of chikungunya virus during a large chikungunya outbreak in Italy: a modeling analysis

Cited by 23 publications

References 28 publications

Diel activity patterns of vector mosquito species in the urban environment: Implications for vector control strategies

Diel activity patterns of vector mosquito species in the urban environment: Implications for vector control strategies

Estimation of the incubation period and generation time of SARS-CoV-2 Alpha and Delta variants from contact tracing data

Estimating SARS‐CoV‐2 transmission in educational settings: A retrospective cohort study

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