<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Impact of human mobility and networking on spread of COVID-19 at the time of the 1st and 2nd epidemic waves in Japan: An effective distance approach."
Abstract:"BackgroundThe influence of human mobility to the domestic spread of COVID-19 in Japan using the approach of effective distance has not yet been assessed. MethodsWe calculated the effective distance between prefectures using the data on laboratory-confirmed cases of COVID-19 from January 16 to August 23, 2020, that were times in the 1(st) and the 2(nd) epidemic waves in Japan. We also used the aggregated data on passenger volume by transportation mode for the 47 prefectures, as well as those in the private railway, bus, ship, and aviation categories. The starting location (prefecture) was defined as Kanagawa and as Tokyo for the 1(st) and the 2(nd) waves, respectively. The accuracy of the spread models was evaluated using the correlation between time of arrival and effective distance, calculated according to the different starting locations. ResultsThe number of cases in the analysis was 16,226 and 50,539 in the 1(st) and 2(nd) epidemic waves, respectively. The relationship between arrival time and geographical distance shows that the coefficient of determination was R-2 = 0.0523 if geographical distance D-geo and time of arrival T-a set to zero at Kanagawa and was R-2 = 0.0109 if D-geo and T-a set to zero at Tokyo. The relationship between arrival time and effective distance shows that the coefficient of determination was R-2 = 0.3227 if effective distance D-eff and T-a set to zero at Kanagawa and was R-2 = 0.415 if D-eff and time of arrival T-a set to zero at Tokyo. In other words, the effective distance taking into account the mobility network shows the spatiotemporal characteristics of the spread of infection better than geographical distance. The correlation of arrival time to effective distance showed the possibility of spreading from multiple areas in the 1(st) epidemic wave. On the other hand, the correlation of arrival time to effective distance showed the possibility of spreading from a specific area in the 2(nd) epidemic wave. ConclusionsThe spread of COVID-19 in Japan was affected by the mobility network and the 2(nd) epidemic wave is more affected than those of the 1(st) epidemic. The effective distance approach has the impact to estimate the domestic spreading COVID-19.."
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Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

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Q1 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:

Impact of human mobility and networking on spread of COVID-19 at the time of the 1st and 2nd epidemic waves in Japan: An effective distance approach
BackgroundThe influence of human mobility to the domestic spread of COVID-19 in Japan using the approach of effective distance has not yet been assessed. MethodsWe calculated the effective distance between prefectures using the data on laboratory-confirmed cases of COVID-19 from January 16 to August 23, 2020, that were times in the 1(st) and the 2(nd) epidemic waves in Japan. We also used the aggregated data on passenger volume by transportation mode for the 47 prefectures, as well as those in the private railway, bus, ship, and aviation categories. The starting location (prefecture) was defined as Kanagawa and as Tokyo for the 1(st) and the 2(nd) waves, respectively. The accuracy of the spread models was evaluated using the correlation between time of arrival and effective distance, calculated according to the different starting locations. ResultsThe number of cases in the analysis was 16,226 and 50,539 in the 1(st) and 2(nd) epidemic waves, respectively. The relationship between arrival time and geographical distance shows that the coefficient of determination was R-2 = 0.0523 if geographical distance D-geo and time of arrival T-a set to zero at Kanagawa and was R-2 = 0.0109 if D-geo and T-a set to zero at Tokyo. The relationship between arrival time and effective distance shows that the coefficient of determination was R-2 = 0.3227 if effective distance D-eff and T-a set to zero at Kanagawa and was R-2 = 0.415 if D-eff and time of arrival T-a set to zero at Tokyo. In other words, the effective distance taking into account the mobility network shows the spatiotemporal characteristics of the spread of infection better than geographical distance. The correlation of arrival time to effective distance showed the possibility of spreading from multiple areas in the 1(st) epidemic wave. On the other hand, the correlation of arrival time to effective distance showed the possibility of spreading from a specific area in the 2(nd) epidemic wave. ConclusionsThe spread of COVID-19 in Japan was affected by the mobility network and the 2(nd) epidemic wave is more affected than those of the 1(st) epidemic. The effective distance approach has the impact to estimate the domestic spreading COVID-19.