The effect of non-pharmaceutical interventions on COVID-19 outcomes: A heterogeneous age-related generalisation of the SEIR model

Mendes, Jorge M.; Coelho, Pedro Simões

doi:10.1016/j.idm.2023.05.009

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…The debate on the pros and cons of population-wide restrictions enacted during the COVID-19 pandemic is ongoing. On the one hand, data seems to support the positive effects of NPIs ( 6–10 ), particularly in terms of control of virus spread and consequent reduction in mortality ( 10 ). On the other hand, some authors emphasize a range of “side effects” of NPIs, including economic, educational, and health repercussions, disproportionately affecting more vulnerable populations, including children, with little health benefits ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis

Maglietta,

Puntoni,

Caminiti

et al. 2024

Front. Public Health

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BackgroundThe use of Non-Pharmaceutical Interventions (NPIs), such as lockdowns, social distancing and school closures, against the COVID-19 epidemic is debated, particularly for the possible negative effects on vulnerable populations, including children and adolescents. This study therefore aimed to quantify the impact of NPIs on the trend of pediatric hospitalizations during 2 years of pandemic compared to the previous 3 years, also considering two pandemic phases according to the type of adopted NPIs.MethodsThis is a multicenter, quasi-experimental before-after study conducted in 12 hospitals of the Emilia-Romagna Region, Northern Italy, with NPI implementation as the intervention event. The 3 years preceding the beginning of NPI implementation (in March 2020) constituted the pre-pandemic phase. The subsequent 2 years were further subdivided into a school closure phase (up to September 2020) and a subsequent mitigation measures phase with less stringent restrictions. School closure was chosen as delimitation as it particularly concerns young people. Interrupted Time Series (ITS) regression analysis was applied to calculate Hospitalization Rate Ratios (HRR) on the diagnostic categories exhibiting the greatest variation. ITS allows the estimation of changes attributable to an intervention, both in terms of immediate (level change) and sustained (slope change) effects, while accounting for pre-intervention secular trends.ResultsOverall, in the 60 months of the study there were 84,368 cases. Compared to the pre-pandemic years, statistically significant 35 and 19% decreases in hospitalizations were observed during school closure and in the following mitigation measures phase, respectively. The greatest reduction was recorded for “Respiratory Diseases,” whereas the “Mental Disorders” category exhibited a significant increase during mitigation measures. ITS analysis confirms a high reduction of level change during school closure for Respiratory Diseases (HRR 0.19, 95%CI 0.08–0.47) and a similar but smaller significant reduction when mitigation measures were enacted. Level change for Mental Disorders significantly decreased during school closure (HRR 0.50, 95%CI 0.30–0.82) but increased during mitigation measures by 28% (HRR 1.28, 95%CI 0.98–1.69).ConclusionOur findings provide information on the impact of COVID-19 NPIs which may inform public health policies in future health crises, plan effective control and preventative interventions and target resources where needed.

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Section: Introductionmentioning

confidence: 99%

Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis

Maglietta,

Puntoni,

Caminiti

et al. 2024

Front. Public Health

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“…However, recent research has been using stochastic approaches (e.g. Ward et al (2022) and Mendes & Coelho (2023)) for modelling the spread of diseases in real-world settings. The main assumption of compartmental models is homogeneous mixing, which assumes that individuals have equal contact probabilities with one another, regardless of their location or social context.…”

Section: Introductionmentioning

confidence: 99%

Bridging Compartmental Models and Network Analysis in Epidemiological Modelling

M Mendes,

Baptista,

MacNab

2024

Epidemiol Public Health

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In this study, the authors undertake a comparative analysis of compartmental models and network analysis as means of simulating the propagation of infectious diseases. Compartmental models operate under the assumption of homogeneous mixing, which is often a flawed assumption as individuals tend to engage in varied contact patterns based on their surrounding environments. On the other hand, social network analysis accounts for the intricate web of interpersonal connections between individuals, thereby offering a more realistic portrayal of social behaviour. However, network analysis can be computationally demanding, rendering its application in real-time epidemic modelling challenging. By conducting a series of simulations utilising the SIR model and network analysis, the authors accentuate the merits of a hybrid modelling approach that integrates the strengths of both compartmental models and network analysis while mitigating their respective limitations. Additionally, the authors suggest plausible avenues for future research.

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“…Researchers who have explored the impact of non-pharmaceutical interventions in models of COVID-19 spread with different age groups typically distinguish the age groups based on susceptibility to infection, contact rates, and the probability of severe outcome or death upon infection, but do not consider the impact of age-group-specific recovery rates [20][21][22][23][24][25][26][27]. When such models do allow for longer recovery times for older or more vulnerable individuals, including in models that specifically seek to represent vulnerable individuals residing in care homes, the models are highly-detailed in structure, involving many disease-state compartments and associated parameters [28][29][30][31][32][33][34][35][36][37][38]; the authors of these studies have not made comprehensive explorations of the model results across broad ranges of parameter values, but rather investigate model outcomes for different intervention scenarios using narrow ranges of epidemiological parameter values taken to be relevant to COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Predictions from standard epidemiological models of consequences of segregating and isolating vulnerable people into care facilities

Hickey,

Rancourt

2023

PLoS ONE

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Objectives Since the declaration of the COVID-19 pandemic, many governments have imposed policies to reduce contacts between people who are presumed to be particularly vulnerable to dying from respiratory illnesses and the rest of the population. These policies typically address vulnerable individuals concentrated in centralized care facilities and entail limiting social contacts with visitors, staff members, and other care home residents. We use a standard epidemiological model to investigate the impact of such circumstances on the predicted infectious disease attack rates, for interacting robust and vulnerable populations. Methods We implement a general susceptible-infectious-recovered (SIR) compartmental model with two populations: robust and vulnerable. The key model parameters are the per-individual frequencies of within-group (robust-robust and vulnerable-vulnerable) and between-group (robust-vulnerable and vulnerable-robust) infectious-susceptible contacts and the recovery times of individuals in the two groups, which can be significantly longer for vulnerable people. Results Across a large range of possible model parameters including degrees of segregation versus intermingling of vulnerable and robust individuals, we find that concentrating the most vulnerable into centralized care facilities virtually always increases the infectious disease attack rate in the vulnerable group, without significant benefit to the robust group. Conclusions Isolated care homes of vulnerable residents are predicted to be the worst possible mixing circumstances for reducing harm in epidemic or pandemic conditions.

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The effect of non-pharmaceutical interventions on COVID-19 outcomes: A heterogeneous age-related generalisation of the SEIR model

Cited by 4 publications

References 45 publications

Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis

Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis

Bridging Compartmental Models and Network Analysis in Epidemiological Modelling

Predictions from standard epidemiological models of consequences of segregating and isolating vulnerable people into care facilities

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