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Malta tourism losses due to second wave of COVID-19

ABSTRACT: The world continues in the grip of COVID-19 with devastated tourism industries and global economies. In a previous paper, it was noted that a country's failure to dampen a first wave of infection or the recurrence of a second wave would serve as disincentives for greatly needed tourists in summer 2020 and would further significantly reduce tourism revenues and potentially accelerate job losses and bankruptcies in affected countries. Countries in the first wave of infection would need to restrain COVID-19 spread swiftly in order to benefit from summer 2020 tourism. Countries that had controlled COVID-19 and who experienced second waves would manifest the same negative effects. In the case of Malta, up to the beginning of July, the country had the lowest COVID-19 numbers in Europe but this ended abruptly when two mass events took place. In a fortnight, the steep escalation of cases led to a downgrade of the country's status to a high-risk destination, with a host of European countries enacting quarantine measures. The Maltese government re-imposed restrictions and COVID-19 numbers slowly started to temporarily decline. As an economy, Malta is highly dependent on the tourism industry, with approximately 17% of GDP reliant on this sector, directly and indirectly. Malta's red listing wrought a heavy toll on the industry. The World Health Organisation has mandated clear criteria for the release of restrictions and this sequence of events should serve as a cautionary tale: heed the advice of our public health colleagues. Highlights • COVID-19 has devastated tourism and global economies.• Tourism is greatly needed to revive economies.• High levels of COVID-19 are tourist disincentives for summer 2020.• Malta had low COVID-19 numbers but two July mass events changed this.• An initial spike in tourism in the first half of August fell drastically thereafter.

SUBMITTER: Grech V

PROVIDER: S-EPMC7528869 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Project description:Marissa Renardy and Denise Kirschner University of Michigan Medical School The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. These heteroge- neous patterns are also observed within individual states, with patches of concentrated outbreaks. Data is being generated daily at all of these spatial scales, and answers to questions regarded re- opening strategies are desperately needed. Mathematical modeling is useful in exactly these cases, and using modeling at a county scale may be valuable to further predict disease dynamics for the purposes of public health interventions. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, the home to University of Michigan, Eastern Michigan University, and Google, as well as serving as a sister city to Detroit, MI where there has been a serious outbreak. Here, we apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact net- works based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We also perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases on COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. Through simulations and sensitivity analyses, we explore mechanisms driving magnitude and timing of a second wave of infections upon re-opening. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.

Project description:The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, which is home to University of Michigan and Eastern Michigan University, and in close proximity to Detroit, MI, a major epicenter of the epidemic in Michigan. We apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact networks based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons or long term care facilities). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases of COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. We find that delaying reopening does not reduce the magnitude of the second peak of cases, but only delays it. Reducing levels of casual contact, on the other hand, both delays and lowers the second peak. Through simulations and sensitivity analyses, we explore mechanisms driving the magnitude and timing of a second wave of infections upon re-opening. We find that the most significant factors are workplace and casual contacts and protective measures taken by infected individuals who have sought care. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.

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Malta tourism losses due to second wave of COVID-19

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