You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:

- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.
- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.
- Prevention: The content covers measures to prevent the spread of COVID-19.
- Mechanism: The paragraph explains the biological mechanisms of the virus.
- Case Report: The content is a report of a COVID-19 case.
- Transmission: The paragraph discusses how COVID-19 is transmitted.
- Diagnosis: The content is about diagnosing COVID-19 in individuals.

Your classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: ["Topic1", "Topic2", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.

Article:

AIMS: During the first wave of the COVID-19 pandemic, Sweden registered a high level of excess deaths. Non-pharmaceutical interventions adopted by Sweden have been milder compared to those implemented in Denmark. Moreover, Sweden might have started the pandemic with a large proportion of vulnerable elderly with a high mortality risk. This study aimed to clarify whether excess mortality in Sweden can be explained by a large stock of 'dry tinder' instead of being attributed to faulty lockdown policies. METHODS: We analysed weekly death counts in Sweden and Denmark from July 2007 to June 2020. We used a novel method for short-term mortality forecasting to estimate expected and excess deaths during the first COVID-19 wave in Sweden and Denmark. RESULTS: In the first part of the epiyear 2019-2020, deaths were low in both Sweden and Denmark. In the absence of COVID-19, a relatively low level of death would be expected for the later part of the epiyear. The registered deaths were, however, way above the upper bound of the prediction interval in Sweden and within the range in Denmark. CONCLUSIONS: 'Dry tinder' can only account for a modest fraction of excess Swedish mortality. The risk of death during the first COVID-19 wave rose significantly for Swedish women aged >85 but only slightly for Danish women aged >85. The risk discrepancy seems more likely to result from differences between Sweden and Denmark in how care and housing for the elderly are organised, coupled with a less successful Swedish strategy of shielding the elderly.
["Prevention"]