The recent coronavirus pandemic follows in its early stages an almost exponential expansion , with the number of cases as a function of time reasonably well fit by N(t)\propto e^{\alpha t}, in many countries. We analyze the rate \alpha in different countries, choosing as a starting point in each countrythe first day with 30 cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way . We look for a link between the rate \alpha and the average temperature T of each country, in the month of the epidemic growth. We analyze a {\it base} set of 42 countries, which developed the epidemic earlier, {\it   and an {\it extended} set of 88 countries, which developed the epidemic more recently. Fitting with a linear behavior \alpha(T), we find evidence in both datasets for a decreasing growth rate as a function of T, at 99.66\\%DIF < C.L. and 99.86\\%C.L. in the {\it base} and {\it extended} dataset, respectively. In the {\it base} set, going beyond a linear model, a peak at about (7.7\pm 3.6)^\circ C seems to be present in the data. Our findings give hope that, for northern hemisphere countries, the growth rate should significantly decrease as a result of both warmer weather and lockdown policies. In general the propagation should be hopefully stopped by strong lockdown, testing and tracking policies, before the arrival of the next cold season.\end{abstract} %DIF > C.L., 99.86\\%C.L. and 99.99995 \\% C.L. (p-value 5 \cdot 10^{-7}, or 5\sigma detection) in the {\it base}, {\it intermediate} and {\it extended} dataset, respectively. The doubling time is expected to increase by 40\\%\sim 50\\%, going from 5^\circ C to 25^\circ C. In the {\it base} set, going beyond a linear model, a peak at (7.7\pm 3.6)^\circ C seems to be present, but its evidence disappears for the larger datasets. We also analyzed a possible bias: poor countries, often located in warm regions, might have less intense testing. By excluding countries below a given GDP per capita, we find that our conclusions are only slightly affected and only for the {\it extended} dataset. The significance remains high, with a p-value of 10^{-3}-10^{-4} or less. Our findings give hope that, for northern hemisphere countries, the growth rate should significantly decrease as a result of both warmer weather and lockdown policies. In general the propagation should be hopefully stopped by strong lockdown, testing and tracking policies, before the arrival of the cold season.
The recent coronavirus pandemic follows in its early stages an almost exponential growth , with the number of cases quite well fit in time by N(t)\propto e^{\alpha t}, in many countries. We analyze the rate \alpha for each country, starting from a threshold of 30 total cases and using the next 12 days, capturing thus the early growth homogeneously . We look for a link between \alpha and the average temperature T of each country, in the month of the epidemic growth. We analyze a {\it base} set of 42 countries, which developed the epidemic earlier, an{\it intermediate set of 88 countries and an {\it extended} set of 125 countries, which developed the epidemic more recently. Applying a linear fit \alpha(T), we find increasing evidence for a decreasing \alpha as a function of T, at 99.66\\%DIF < C.L. and 99.86\\%C.L. in the {\it base} and {\it extended} dataset, respectively. In the {\it base} set, going beyond a linear model, a peak at about (7.7\pm 3.6)^\circ C seems to be present in the data. Our findings give hope that, for northern hemisphere countries, the growth rate should significantly decrease as a result of both warmer weather and lockdown policies. In general the propagation should be hopefully stopped by strong lockdown, testing and tracking policies, before the arrival of the next cold season.\end{abstract} %DIF > C.L., 99.86\\%C.L. and 99.99995 \\% C.L. (p-value 5 \cdot 10^{-7}, or 5\sigma detection) in the {\it base}, {\it intermediate} and {\it extended} dataset, respectively. The doubling time is expected to increase by 40\\%\sim 50\\%, going from 5^\circ C to 25^\circ C. In the {\it base} set, going beyond a linear model, a peak at (7.7\pm 3.6)^\circ C seems to be present, but its evidence disappears for the larger datasets. We also analyzed a possible bias: poor countries, often located in warm regions, might have less intense testing. By excluding countries below a given GDP per capita, we find that our conclusions are only slightly affected and only for the {\it extended} dataset. The significance remains high, with a p-value of 10^{-3}-10^{-4} or less. Our findings give hope that, for northern hemisphere countries, the growth rate should significantly decrease as a result of both warmer weather and lockdown policies. In general the propagation should be hopefully stopped by strong lockdown, testing and tracking policies, before the arrival of the cold season.
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expansion
growth
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as a function of time reasonably well fit
quite well fit in time
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in different countries, choosing as a starting point in each countrythe first day with
for each country, starting from a threshold of
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cases and fitting for the following
total cases and using the next
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exponential growth in a rather homogeneous way
growth homogeneously
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the rate
A
an
A
intermediate
A
set of 88 countries
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88
125
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Fitting with a linear behavior
Applying a linear fit
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evidence in both datasets
increasing evidence
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growth rate
\alpha