CoMix: comparing mixing patterns in the Belgian population during and after lockdown

Coletti, Pietro; Wambua, James; Gimma, Amy; Willem, Lander; Vercruysse, Sarah; Vanhoutte, Bieke; Jarvis, Christopher; Zandvoort, Kevin van; Edmunds, John; Beutels, Philippe; Hens, Niel

doi:10.1101/2020.08.06.20169763

Cited by 39 publications

(85 citation statements)

References 27 publications

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“…As of Nov 30, total estimated attack rates are 20.7%(95%CI : 17.3%−24.1%) in Rhode Island and 12.5% (95% CI : 11.5% − 13.5%) in Massachusetts [ 21 ]. We upgraded the model parameterization by using age-contact matrices measured in Belgium (CoMix) which are representative of lockdown and post-lockdown mixing patterns in western countries [ 22 ]. The eight age-bands in the CoMix data [ 22 ] were transformed into our nine age-bands using the socialmixr R package [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

“…We upgraded the model parameterization by using age-contact matrices measured in Belgium (CoMix) which are representative of lockdown and post-lockdown mixing patterns in western countries [ 22 ]. The eight age-bands in the CoMix data [ 22 ] were transformed into our nine age-bands using the socialmixr R package [ 23 ]. Using median values from posteriors from the new model fit, the new model’s Nov 30 attack rates are 16.8% for RI and 16.5% for MA.…”

Section: Methodsmentioning

confidence: 99%

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Optimal SARS-CoV-2 vaccine allocation using real-time seroprevalence estimates in Rhode Island and Massachusetts

Tran

Wikle

Albert

et al. 2021

Preprint

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As three SARS-CoV-2 vaccines come to market in Europe and North America in the winter of 2020-2021, distribution networks will be in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation is critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs require that distribution is prioritized to the elderly, health-care workers, teachers, essential workers, and individuals with co-morbidities putting them at risk of severe clinical progression. Here, we evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not be included in the first round of vaccination. And, we account for current age-specific immune patterns in both states. We find that allocating a substantial proportion (> 75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. As we do not explicitly model other high mortality groups, this result on vaccine allocation applies to all groups at high risk of mortality if infected. Our analysis confirms that for an easily transmissible respiratory virus, allocating a large majority of vaccinations to groups with the highest mortality risk is optimal. Our analysis assumes that health systems during winter 2020-2021 have equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. Vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and will result in 1% to 2% reductions in cumulative hospitalizations and deaths by mid-2021. Assuming high vaccination coverage (> 28%) and no major relaxations in distancing, masking, gathering size, or hygiene guidelines between now and spring 2021, our model predicts that a combination of vaccination and population immunity will lead to low or near-zero transmission levels by the second quarter of 2021.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Optimal SARS-CoV-2 vaccine allocation using real-time seroprevalence estimates in Rhode Island and Massachusetts

Tran

Wikle

Albert

et al. 2021

Preprint

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“…In our study, we found that adherence was strongly correlated with individuals' symptoms compatible with COVID-19, as well as their age. Whether better adherence by younger age groups was biased by their lower and decreasing response rate and that therefore mainly those that are health-conscious participated in later waves, or the fact that people in older age groups have fewer contacts [8,9] and might not have defined their lack of contacts as 'quarantine/isolation', or whether this is a real effect, is yet unknown. Our findings on age differences were similar to observations made in Italy [10].…”

Section: A Covid-19-compatible Symptoms B Age Groups In Yearsmentioning

confidence: 99%

Poor self-reported adherence to COVID-19-related quarantine/isolation requests, Norway, April to July 2020

et al. 2020

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To limit SARS-CoV-2 spread, quarantine and isolation are obligatory in several situations in Norway. We found low self-reported adherence to requested measures among 1,704 individuals (42%; 95% confidence interval: 37-48). Adherence was lower in May-June-July (33-38%) compared with April (66%), and higher among those experiencing COVID-19-compatible symptoms (71%) compared with those without (28%). These findings suggest that consideration is required of strategies to improve people's adherence to quarantine and isolation.

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“…Thus, we used nine independent mixing rates for the nine age-classes in our model (and assumed that contact between two age groups is proportional to their two mixing rates); the data are unlikely to have enough resolution to infer 81 independent mixing parameters. As confidence grows in the survey methods used to collect post-pandemic age-contact data[37][38][39][40][41] these matrices should be included into modeling efforts like ours.Infectious disease epidemiology has been neglected in the United States for more than half a century because of our status as a developed country, with a secure food supply, a sanitary water system, few persistent disease vectors, high public hygiene standards, and ample supply of therapeutics and vaccines. We were not prepared in 1981 when the HIV epidemic was uncovered, and with no leadership from the federal government in 2020, we are underprepared at the state level for the SARS-CoV-2 epidemic as few individuals remain with knowledge from the early struggle against HIV.…”

mentioning

confidence: 99%

SARS-CoV-2 epidemic after social and economic reopening in three US states reveals shifts in age structure and clinical characteristics

Wikle

Tran

Gentilesco

et al. 2020

Preprint

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In the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission. One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices. A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases. Here, we analyze age-structured case, hospitalization, and death time series from three states – Rhode Island, Massachusetts, and Pennsylvania – that had successful re-openings in May 2020 without summer waves of infection. Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mobility was broken in May as these states partially re-opened. We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.0% (RI), 72.1% (MA), and 75.5% (PA); in Rhode Island, when accounting for cases caught through general-population screening programs, the reporting rate estimate is 94.5%. We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals, leading to the outbreak being concentrated in elderly congregate settings despite the lockdown. Attack rate estimates through August 31 2020 are 6.4% (95% CI: 5.8% − 7.3%) of the total population infected for Rhode Island, 5.7% (95% CI: 5.0% − 6.8%) in Massachusetts, and 3.7% (95% CI: 3.1% − 4.5%) in Pennsylvania, with some validation available through published seroprevalence studies. Infection fatality rates (IFR) estimates are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations, especially the most vulnerable of the ≥80 age group. We make several suggestions for enhancements to current data collection practices that could improve response efforts in winter.

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CoMix: comparing mixing patterns in the Belgian population during and after lockdown

Cited by 39 publications

References 27 publications

Optimal SARS-CoV-2 vaccine allocation using real-time seroprevalence estimates in Rhode Island and Massachusetts

Optimal SARS-CoV-2 vaccine allocation using real-time seroprevalence estimates in Rhode Island and Massachusetts

Poor self-reported adherence to COVID-19-related quarantine/isolation requests, Norway, April to July 2020

SARS-CoV-2 epidemic after social and economic reopening in three US states reveals shifts in age structure and clinical characteristics

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