Between Geography and Demography: Key Interdependencies and Exit Mechanisms for Covid-19

Antonio Scala; Andrea Flori; Alessandro Spelta; Emanuele Brugnoli; Matteo Cinelli; Walter Quattrociocchi; Fabio Pammolli

As we have already observed in the previous Section, heterogeneity strongly impacts on the results of a model [31] . Since the transmission coefficient is proportional to the contact rate of individuals, the rates of social mixing among different age classes could represent another important source of heterogeneity. This kind of information has been estimated either through large-scales surveys [24] or through virtual populations modeling [32] . While the POLYMOD [24] matrices have been extensively used to estimate the cost-effectiveness of vaccination for different age-classes during the 2009 H1N1 pandemic [33, 34] , here we apply such matrices to the design of lockdown measures and exit strategies. Hence, to account for age classes, we extend our model by rewriting the transmission coefficient as βC (see Sec. 8.7), where β is the transmission probability characteristic of the disease, and C is the sociological matrix describing the contact patterns typical of a nation. For lack of further information, we assume β constant among age classes and C as in [24] . To simplify the analysis, we gather POLYMOD age groups into three classes: Y oung (00 − 19), M iddle (20 − 69) and Elderly (70+) (see Tab 2) . Such aggregation puts together the most "contactful" classes (00 − 19), the classes with the highest mortality risk (70+) [19] , and the working class (20 − 69). 12 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.