The spreading of virus infection is here simulated over artificial human networks. Here, the real-space urban life of people is modeled as a scale-free network with constraints. A scale-free network has been adopted for modeling on-line communities so far but is employed here for the aim to represent peoples' social behaviors where the generated communities are restricted reflecting the spatiotemporal constraints in the real life. As a result, three findings and a policy proposal have been obtained. First, the height of the peaks in the time sequence of the number of infection cases tends to get reduced corresponding to the upper bound of the size of groups where all members meet. Second, if we adopt the constraint on m0, the number of all other people one meets separately each at a time, to the range between 2 and 8, its effect on the suppression of infections may be weak as far as we allow group meetings of size W of 8 or larger. Third, such a moderate constraint may temporarily seem to work for the reduction of infections in the early stage but it may turn out to be just a delay of peaks. Based on these results, a policy is proposed here: for quickly suppressing the number of infections, restrict W to less than 4 if the constraint to make m0 at most 1 is too strict. If W is set to less than 4, setting m0 to 4 or less works for quick reduction of infections according to the result.