Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City

Wilder, Bryan; Charpignon, Marie-Laure; Killian, Jackson A.; Ou, Han Ching; Mate, Aditya; Jabbari, Shahin; Perrault, Andrew; Desai, Angel N.; Tambe, Milind; Majumder, Maimuna S.

doi:10.1073/pnas.2010651117

Cited by 50 publications

(58 citation statements)

References 36 publications

(48 reference statements)

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“…In the sample of papers under review here, we find full fledged agent-based models used to model the COVID-19 and the effects of NPIs at the levels of countries, regions, or cities [63] , [66] , [135] , [137] , [138] , [157] . We also find more theoretical approaches modeling particular aspects of NPIs such as different strategies for isolation [136] , [139] , [140] .…”

Section: Epidemic Modelsmentioning

confidence: 99%

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Non-pharmaceutical interventions during the COVID-19 pandemic: A review

2021

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Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travels bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 348 articles written by more than 2518 of authors in the first 12 months of the emergency. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities.

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Section: Epidemic Modelsmentioning

confidence: 99%

“…Authors of Ref. [135] propose an agent-based model to study the spreading of COVID-19 in Hubei, Lombardy (Italy), and New York City. The model nicely show how the details can be adapted to the goal and data available.…”

Section: Epidemic Modelsmentioning

confidence: 99%

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

2021

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“…In the example evaluated for Spain, population groups were defined by (age-related) activity as namely: preschool children (ages 0-4); school children (ages [5][6][7][8][9][10][11][12][13][14]; higher school and university young (ages [15][16][17][18][19][20][21][22][23][24], young workers (ages 25-49); mature workers (ages 50-59); senior workers (ages 60-64); early retired (ages 65-69); retired (ages 70-79); elderly (ages 80+). This definition of groups is partly arbitrary but based on the criteria of meaningful differences in mortality and behaviour that impact the problem at hand.…”

Section: Model Implementation For Spainmentioning

confidence: 99%

“…Decisions based on empirical observations are a luxury not always possible during a pandemic and mathematical modelling provides a tool to guide immediate decisions. Although several epidemic models for the COVID-19 outbreak have been developed to forecast the extent of the pandemic or the impact of interventions for different regions (14)(15)(16)(17)(18)(19)(20)(21), any predictive mathematical model has limitations, which are inherent to the model itself but also to the quality of the input data. Models can lead to great predictive failures (22) however they can also serve as valuable tools to better understand nontrivial underlying interactions in complex processes and to comparatively evaluate public health strategies.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 vaccination rate and protection attitudes can determine the best prioritisation strategy to reduce fatalities

Rodríguez

Patón

Acuña

2020

Preprint

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The rapid development of vaccines against the SARS-CoV-2 virus is an unprecedented achievement. Once vaccines become mass produced, they will have to be distributed to almost the entire population to prevent deaths and permit prompt economic recovery. The necessity to vaccinate a large number of people in a short period of time, and possibly with insufficient vaccine doses to cover most, creates in itself a new challenge for governments and health authorities: which population groups (by age or other criteria) should be targeted first and what sequence must be followed, if any at all, to achieve the minimum number of fatalities? In this work, we demonstrate the importance and impact of optimally planning the priorities for vaccine deployment by population groups using a modified SEIR-type model for the COVID-19 outbreak considering age-related groups. Finding the absolute guaranteed best solution of the mathematical optimisation problem may be hard, if even possible, and would likely require intense computational resources for every possible case study scenario. In this work, several strategies are evaluated and compared, in an attempt to approach the most effective possible vaccination priority sequence in an example case study using demographic and epidemiological data from Spain. The minimum total fatalities at the end of the vaccination campaign is the objective pursued. The population groups classifications are established based on relevant differences in mortality (due to their age) and risk-related behaviour such as their number of daily person-to-person interactions. Assuming a capacity limited constant vaccination rate, vaccination distribution strategies were evaluated for different vaccine effectiveness levels and different percentages of final vaccine population coverage. Our results unambiguously show how planning vaccination by priority groups can achieve dramatic reductions in total fatalities (more than 70% in some cases) compared to no prioritisation. The results also indicate in all cases, for all vaccine effectiveness and coverage values evaluated, that the criteria for groups vaccination priority should not be those with the highest mortality but rather those the highest number of daily person-to-person interactions. Strikingly, our results show in all cases, that prioritisation of groups with the highest mortality but less social interactions, may lead to significantly larger numbers of final total fatalities, even higher as if no group priorities were established at all. The explanation, clearly displayed by the mechanistic model, is that vaccination avoids infections that reduce mortality not only from the vaccinated group itself but also from the projected secondary and subsequent infections inflicted on the rest of the population by those vaccinated in that group. Precisely this amplification effect (exponential nature of the curve) appears to cause the larger reduction in total fatalities if the groups with the most interactions are vaccinated first. The possible contradiction of these results with some published recommendations highlight the importance of conducting an open comprehensive and rigorous analysis of this problem leaving behind any subjective preconceptions.

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“…An additional component of population heterogeneity not treated in this work is age dependence, which is known to be particularly important for modelling the COVID-19 pandemic. When considering expanded models that include age compartments, various mixing mechanisms across age groups generate different reproductive rates of infection [ 25 – 27 ]. One extreme compartmented grouping is to decompose a population into young, middle aged, and seniors with age-dependent contact rates between groups, age-dependent recovery periods, and some modest age-dependence in incubation periods.…”

Section: Discussionmentioning

confidence: 99%

Optimal periodic closure for minimizing risk in emerging disease outbreaks

2021

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Without vaccines and treatments, societies must rely on non-pharmaceutical intervention strategies to control the spread of emerging diseases such as COVID-19. Though complete lockdown is epidemiologically effective, because it eliminates infectious contacts, it comes with significant costs. Several recent studies have suggested that a plausible compromise strategy for minimizing epidemic risk is periodic closure, in which populations oscillate between wide-spread social restrictions and relaxation. However, no underlying theory has been proposed to predict and explain optimal closure periods as a function of epidemiological and social parameters. In this work we develop such an analytical theory for SEIR-like model diseases, showing how characteristic closure periods emerge that minimize the total outbreak, and increase predictably with the reproductive number and incubation periods of a disease– as long as both are within predictable limits. Using our approach we demonstrate a sweet-spot effect in which optimal periodic closure is maximally effective for diseases with similar incubation and recovery periods. Our results compare well to numerical simulations, including in COVID-19 models where infectivity and recovery show significant variation.

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Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City

Cited by 50 publications

References 36 publications

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

COVID-19 vaccination rate and protection attitudes can determine the best prioritisation strategy to reduce fatalities

Optimal periodic closure for minimizing risk in emerging disease outbreaks

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