Conditions for a second wave of COVID-19 due to interactions between disease dynamics and social processes

Pedro, Sansao A.; Ndjomatchoua, Frank T.; Jentsch, P; Tcheunche, Jean M; Anand, Madhur; Bauch, Chris T.

doi:10.1101/2020.05.22.20110502

Cited by 41 publications

(44 citation statements)

References 50 publications

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“…They exemplify coupled social–epidemiological systems in which disease dynamics and behavioural dynamics interact with one another. 14 …”

Section: Introductionmentioning

confidence: 99%

“… 15 , 16 , 17 , 18 , 19 Some models have used evolutionary game theory to model this two-way feedback in a variety of coupled human–environment systems. 14 , 20 , 21 , 22 , 23 , 24 , 25 Evolutionary game theory captures how individuals learn social behaviours from others while weighing risks and benefits of different choices. In this framework, individuals who do not adopt non-pharmaceutical interventions can free-ride on the benefits of reduced transmission generated by individuals who do adopt non-pharmaceutical interventions.…”

Section: Introductionmentioning

confidence: 99%

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Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study

Jentsch

Anand

Bauch

2021

The Lancet Infectious Diseases

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195

128

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Background During the COVID-19 pandemic, authorities must decide which groups to prioritise for vaccination in a shifting social–epidemiological landscape in which the success of large-scale non-pharmaceutical interventions requires broad social acceptance. We aimed to compare projected COVID-19 mortality under four different strategies for the prioritisation of SARS-CoV-2 vaccines. Methods We developed a coupled social–epidemiological model of SARS-CoV-2 transmission in which social and epidemiological dynamics interact with one another. We modelled how population adherence to non-pharmaceutical interventions responds to case incidence. In the model, schools and workplaces are also closed and reopened on the basis of reported cases. The model was parameterised with data on COVID-19 cases and mortality, SARS-CoV-2 seroprevalence, population mobility, and demography from Ontario, Canada (population 14·5 million). Disease progression parameters came from the SARS-CoV-2 epidemiological literature. We assumed a vaccine with 75% efficacy against disease and transmissibility. We compared vaccinating those aged 60 years and older first (oldest-first strategy), vaccinating those younger than 20 years first (youngest-first strategy), vaccinating uniformly by age (uniform strategy), and a novel contact-based strategy. The latter three strategies interrupt transmission, whereas the first targets a vulnerable group to reduce disease. Vaccination rates ranged from 0·5% to 5% of the population per week, beginning on either Jan 1 or Sept 1, 2021. Findings Case notifications, non-pharmaceutical intervention adherence, and lockdown undergo successive waves that interact with the timing of the vaccine programme to determine the relative effectiveness of the four strategies. Transmission-interrupting strategies become relatively more effective with time as herd immunity builds. The model predicts that, in the absence of vaccination, 72 000 deaths (95% credible interval 40 000–122 000) would occur in Ontario from Jan 1, 2021, to March 14, 2025, and at a vaccination rate of 1·5% of the population per week, the oldest-first strategy would reduce COVID-19 mortality by 90·8% on average (followed by 89·5% in the uniform, 88·9% in the contact-based, and 88·2% in the youngest-first strategies). 60 000 deaths (31 000–108 000) would occur from Sept 1, 2021, to March 14, 2025, in the absence of vaccination, and the contact-based strategy would reduce COVID-19 mortality by 92·6% on average (followed by 92·1% in the uniform, 91·0% in the oldest-first, and 88·3% in the youngest-first strategies) at a vaccination rate of 1·5% of the population per week. Interpretation The most effective vaccination strategy for reducing mortality due to COVID-19 depends on the time course of the pandemic in the population. For later vaccination start dates, use of SARS-CoV-2 vaccines to interrupt transmission might prevent more deaths than prioritising vulnera...

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“…They exemplify coupled social–epidemiological systems in which disease dynamics and behavioural dynamics interact with one another. 14 …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study

Jentsch

Anand

Bauch

2021

The Lancet Infectious Diseases

Self Cite

195

128

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show abstract

“…The authors are grateful to comments from two reviewers. This manuscript has been released as a pre-print at medRxiv [59].…”

Section: Data Availability Statementmentioning

confidence: 99%

Conditions for a Second Wave of COVID-19 Due to Interactions Between Disease Dynamics and Social Processes

et al. 2020

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“…Approaches to modelling coupled social-epidemiological dynamics vary (15)(16)(17)(18)(19). Some previous models have used evolutionary game theory (EGT) to model this two-way feedback in a variety of coupled humanenvironment systems (14,(20)(21)(22)(23)(24)(25). EGT is relevant to population adherence to NPIs since it captures how individuals learn social behaviours from one another while weighting personal risks and benefits of different choices.…”

Section: Introductionmentioning

confidence: 99%

Prioritising COVID-19 vaccination in changing social and epidemiological landscapes

Jentsch

Anand

Bauch

2020

Preprint

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During the COVID-19 pandemic, authorities must decide which groups to prioritise for vaccination. These decision will occur in a constantly shifting social-epidemiological landscape where the success of large-scale non-pharmaceutical interventions (NPIs) like physical distancing requires broad population acceptance. We developed a coupled social-epidemiological model of SARS-CoV-2 transmission. Schools and workplaces are closed and re-opened based on reported cases. We used evolutionary game theory and mobility data to model individual adherence to NPIs. We explored the impact of vaccinating 60+ year-olds first; <20 year-olds first; uniformly by age; and a novel contact-based strategy. The last three strategies interrupt transmission while the first targets a vulnerable group. Vaccination rates ranged from 0.5% to 4.5% of the population per week, beginning in January or July 2021. Case notifications, NPI adherence, and lockdown periods undergo successive waves during the simulated pandemic. Vaccination reduces median deaths by 32%-77% (22%-63%) for January (July) availability, depending on the scenario. Vaccinating 60+ year-olds first prevents more deaths (up to 8% more) than transmission-interrupting strategies for January vaccine availability across most parameter regimes. In contrast, transmission-interrupting strategies prevent up to 33% more deaths than vaccinating 60+ year-olds first for July availability, due to higher levels of natural immunity by that time. Sensitivity analysis supports the findings. Further research is urgently needed to determine which populations can benefit from using SARS-CoV-2 vaccines to interrupt transmission.

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Conditions for a second wave of COVID-19 due to interactions between disease dynamics and social processes

Cited by 41 publications

References 50 publications

Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study

Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study

Conditions for a Second Wave of COVID-19 Due to Interactions Between Disease Dynamics and Social Processes

Prioritising COVID-19 vaccination in changing social and epidemiological landscapes

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