Effect of public health interventions during the first epidemic wave of COVID-19 in Cyprus: a modelling study

Abstract

Background Cyprus addressed the first wave of SARS CoV-2 (COVID-19) by implementing non-pharmaceutical interventions (NPIs). The aims of this study were: a) to estimate epidemiological parameters of this wave including infection attack ratio, infection fatality ratio, and case ascertainment ratio, b) to assess the impact of public health interventions and examine what would have happened if those interventions had not been implemented. Methods A dynamic, stochastic, individual-based Susceptible-Exposed-Infected-Recovered (SEIR) model was developed to simulate COVID-19 transmission and progression in the population of the Republic of Cyprus. The model was fitted to the observed trends in COVID-19 deaths and intensive care unit (ICU) bed use. Results By May 8th, 2020, the infection attack ratio was 0.31% (95% Credible Interval [CrI]: 0.15, 0.54%), the infection fatality ratio was 0.71% (95% CrI: 0.44, 1.61%), and the case ascertainment ratio was 33.2% (95% CrI: 19.7, 68.7%). If Cyprus had not implemented any public health measure, the healthcare system would have been overwhelmed by April 14th. The interventions averted 715 (95% CrI: 339, 1235) deaths. If Cyprus had only increased ICU beds, without any social distancing measure, the healthcare system would have been overwhelmed by April 19th. Conclusions The decision of the Cypriot authorities to launch early NPIs limited the burden of the first wave of COVID-19. The findings of these analyses could help address the next waves of COVID-19 in Cyprus and other similar settings.

Socio-economic analysis of short-term trends of COVID-19: modeling and data analytics

“…HCI and age variables: the coupled effect of health care system and elderly According to the literature, the health care system was considered by means of different indicators such as ICU level [11], Prevention and Control (P&C) capacity [71], the number of hospitals and the 4 T's (tracing, tracking, testing and treating) [69]. In this work, and as previously introduced, HCI index was adopted as a public health policy indicator.…”

“…The results of the corresponding simulations showed that the low temperature could allow the proliferation of the viruses but also temperature may cause host shifts (denoted as temperature anomalies) for viruses and increases the susceptibility of more susceptible species. Nevertheless, after including HDI in the models, the environmental factors lost their effects and x x [11] x d x e [15] x [16] x [18] x d [51] x [52] x [53] x [54] x [55] x [56] x [57] x x [58] x [59] x [60] x [61] x…”

“…Furthermore, other researchers tried to figure-out the eventual relationships that might exist between socioeconomic characteristics of countries and the disease kinetics. Most of these researches handle systemic models in terms of time series modeling [11,12], statistical analysis [13,14], stochastic and dynamic analysis using epidemiological modeling [1][2][3][4]. For instance, Nader et al [15] used non-parametric machine learning model to estimate the Non-Pharmaceutical Interventions (NPI) effects on COVID-19 propagation; based on the simulations, the authors summarized numerous conclusions related to short-term pandemic propagation in schools or according to business activities in 176 countries (that was expressed by means of GDP per capita).…”

The impact of multi-level interventions on the second-wave SARS-CoV-2 transmission in China

“…As the COVID-19 vaccine campaign was not launched in mainland China until early 2021, non-pharmaceutical interventions (NPIs) remained the main way to control the second-wave of COVID-19. The effects of NPIs on controlling COVID-19 transmission have been widely confirmed in the first-and second-wave [8][9][10][11][12]. A study from Italy by Manica et al [13] has estimated that the three-tiered restriction system, which was taken by Italian government to counter the national second-wave, effectively lowered the reproduction number below 1 in 85 out of 107 provinces.…”

Construction and validation of a covariate-based model for district-level estimation of excess deaths due to COVID-19 in India

“…Many studies [17][18][19][20][21][22][23] reported population-level covariates for predicting excess deaths in a community. Some of the factors that have been found to be significantly associated include urbanicity; population density; proportion of elderly in the population; presence of specific comorbidities; health system and governance indicators like human resource or bed density; coverage of public health interventions; and indicators of severity of the pandemic.…”