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:

Background: The coronavirus disease 2019 (COVID-19) has taken millions of lives and disrupted living standards at individual, societal, and worldwide levels, causing serious consequences globally. Understanding its epidemic curve and spatio-temporal dynamics is crucial for the development of effective public health plans and responses and the allocation of resources. Thus, we conducted this study to assess the epidemiological dynamics and spatio-temporal patterns of the COVID-19 pandemic in Rwanda. Methods: Using the surveillance package in R software version 4.0.2, we implemented endemic-epidemic multivariate time series models for infectious diseases to analyze COVID-19 data reported by Rwanda Biomedical Center under the Ministry of Health from March 15, 2020 to January 15, 2021. Results: The COVID-19 pandemic occurred in two waves in Rwanda and showed a heterogenous spatial distribution across districts. The Rwandan government responded effectively and efficiently through the implementation of various health measures and intervention policies to drastically reduce the transmission of the disease. Analysis of the three components of the model showed that the most affected districts displayed epidemic components within the area, whereas the effect of epidemic components from spatial neighbors were experienced by the districts that surround the most affected districts. The infection followed the disease endemic trend in other districts. Conclusion: The epidemiological and spatio-temporal dynamics of COVID-19 in Rwanda show that the implementation of measures and interventions contributed significantly to the decrease in COVID-19 transmission within and between districts. This accentuates the critical call for continued intra- and inter- organization and community engagement nationwide to ensure effective and efficient response to the pandemic.
["Prevention"]