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. 2020 Jun 10;8:109719–109731. doi: 10.1109/ACCESS.2020.3001298

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This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

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FIGURE 3. — Parameter sensitivity. The four surfaces explore the expected total number of infections (population ) for various parameter values and (for ) and different control strategies (i.e. for different ). The four surfaces depicted here correspond to (a) ; (b) ; (c) ; (d) (that is, 99%, 90%, 80% and 70% observance of physical distancing measures). The three coordinates are (x) ; (y) ; and (z) (the logarithm base-10 of the total number of infections). In each case we computed 80 simulations of 300 days. Other parameters are as reported in Table 1. Surface (a) and (b) exhibit linear scaling with changing parameter values and , while for (c) and (d) that growth is exponential. That is, when compliance with isolation measures drops below 90% there is an explosive growth in the level of infection with and .

Inline graphic — Parameter sensitivity. The four surfaces explore the expected total number of infections (population ) for various parameter values and (for ) and different control strategies (i.e. for different ). The four surfaces depicted here correspond to (a) ; (b) ; (c) ; (d) (that is, 99%, 90%, 80% and 70% observance of physical distancing measures). The three coordinates are (x) ; (y) ; and (z) (the logarithm base-10 of the total number of infections). In each case we computed 80 simulations of 300 days. Other parameters are as reported in Table 1. Surface (a) and (b) exhibit linear scaling with changing parameter values and , while for (c) and (d) that growth is exponential. That is, when compliance with isolation measures drops below 90% there is an explosive growth in the level of infection with and .