The effects of border control and quarantine measures on global spread of COVID-19

M. Pear Hossain; Alvin Junus; Xiaolin Zhu; Pengfei Jia; Tzai-Hung Wen; Dirk Pfeiffer; Hsiang-Yu Yuan

Constructing contact matrix 126 Airline passenger data were collected from the International Air Transport Association (IATA) database. We 127 collected the actual passenger data for top 10 visiting cities leaving from Wuhan Tianhe International Airport 128 before the lockdown of Wuhan city from 30 December to 20 January, 2020. Note that we did not have data for 129 railroad and other forms of transport, and thus made an assumption that the total number of travelers is 4 times 130 higher than that of air transport except for certain cities on Hainan island that have no road connections to 131 Wuhan. We made this assumption because that the number of train passengers is few times higher than that of 132 airline in China [15] and the results from a population migration database suggested a similar ratio [16] . The 133 number of daily passengers between different cities were used to generate the mobility rate M ji between 134 locations i and j. For example, we divided a daily passenger number by total population size in Wuhan to 135 represent the contact rate between Wuhan city and any other connected city j. We denote i = 0 as the index for 136 the source city. Therefore, M 00 = 1 is the base level of contact rate within the source city. j is a number to 137 represent the index of the top visiting cities from first to last following the order in the list. Because the 138 estimated number of people leaving from Wuhan to the first visiting city (Beijing) is 10793.5 per day, given that 139 the population size of Wuhan is about 11 million, the daily percentage of people leaving to the 1st visiting city 140 can be approximated to 0.001. Thus, we have M 10 = 0.001. We used the same approach to determine M j0 for 141 j = 2, 3, . . . , 10.