For the Philippines, we use model predictions for 14 and 28 days to identify top-40 municipalities. Of the 14-day top 40, 8 are in the top 2% for actual infection growth, 10 are in the top 4%, and 17 are in the top 6%. The corresponding tallies for the 28-day predictions are 5, 6 and 9, respectively. Extrapolation from past infection growth yields 14-day predictions that are notably better than the US extrapolative predictions, but still significantly less accurate than the model-based predictions. To summarize, the model estimates for the Philippines identify hotspots in the top-6% of municipalities with 42.5% accuracy for 14 days and 22.5% accuracy for 28 days. Per our previous discussion for the United States, the model’s 14-day accuracy seems easily good enough to warrant alerts. Its 28-day accuracy is significantly lower, but still high enough to warrant consideration. As always, the relevant question remains, “Compared to what?” For South Africa’s Western Cape province, we use model estimates to identify the future top-10 sub-districts in 14 and 28 days. We find that these estimates identify actual top-10 sub-districts with 70% accuracy for 14 days and 50% accuracy for 28 days. In contrast, extrapolation from past infection growth does no better than random selection. By our previous arguments, these results seem easily good enough to warrant alerts.

To summarize, our estimated models for the United States, the Philippines and South Africa’s Western Cape province produce 14-day and 28-day out-of-sample predictions that seem good enough to warrant their use for community alerts. We believe that all three models could be improved by adding relevant, locally-available variables that would help explain the observed variation in Covid-19 infection rates. At the same time, we have found that the pattern illustrated by our 14-day US analysis holds for all three countries: Actual hotspots are identified with enough accuracy to warrant alerts, but more than a few predicted hotspots have little or no actual change, even if the core model is augmented with locally-significant variables. This persistent bifurcation is an important issue for future research.

For the present, we believe that our results suffice to convey the following message: Any country with a panel of spatially-disaggregated Covid-19 infection data can draw on free, globallyavailable databases to estimate a simple, theoretically-grounded infection spread model based on human spatial interaction. Our evidence from three very different country cases suggests that the results can produce useful alerts for future infections as far as one month in advance.

# **7.  Possible Extensions of the Model**

We have focused on developing and testing a core model that can be estimated with publiclyavailable data in any setting. In our model of Covid-19 growth dynamics, a locale’s population density, time-varying W-value, Gompertz α-value and travel time θ-value determine its growth rate toward a time-varying asymptote. The asymptote for a particular locale undoubtedly has other determinants that are country-specific, and there may well be determinants that belong in the core model because they are universally-available and affect infection transmission in all cases with high significance and the same sign. We have explored both possibilities in a large number of experiments conducted with our “Tukey 1/3” sample set aside for exploration. Among the variables explored in one or more countries are health risk (diabetes and obesity incidence), life expectancy (a proxy for general health conditions), income, ethnicity, employment-related infection risk, demography (percent elderly), temperature and humidity, wind conditions, altitude 16