A Heuristic Model for Spreading of COVID 19 in Singapore

Fook Hou Lee

Less is known about the detection efficiency k. Niehus et al.'s (2020) postulation only relates to the relative (rather than absolute) detection capacity and they noted that Singapore's detection capacity is likely to be less than 100% (or 1.0) and some other countries may be much lower. One way to backestimate k is through the geometric rate of increase. If we assume a median value of r ~1.5, based on the discussion above, then k can be estimated by back-fitting the α-value, Table 1 . As Table 1 shows, Singapore's back-fitted detection efficiency k is significantly higher than that of China (in the early stage) and the US. This is consistent with Niehus et al.'s (2020) postulated that "…the global ability to detect imported cases is 38% (95% HPDI 22% -64%) of Singapore's capacity". The current backfitted results indicate a somewhat higher percentage (53%) instead of 38%. Nonetheless, the trend is generally correct and it explains the slower rise in infected cases in Singapore. Moreover, the detection rate of 35.5% for China is also reasonably consistent with Wang et al.'s (2020) finding that "at least 59% of infected cases were unascertained in Wuhan". The latter would imply a detection efficiency of 41% or less. Fig. 6 shows the cumulative number of cases from dormitory clusters, non-dormitory clusters and unlinked cases from 30 March 2020 till 9 April 2020. As can be seen, both the non-dormitory clusters and unlinked cases plot to almost the same gradient, implying almost equal α-value, which can be fitted using k = 0.54. This suggests a possible decrease in the detection efficiency from the initial high value of 0.67. The alternative explanation is an increase in r-value, but this seems rather unlikely since the social setting, virological characteristics and infectious period have not changed. However, there may be some over-estimation of the unlinked cases as some of these might have been subsequently linked to existing clusters or cases. The dormitory clusters show a much higher α-value. Since the kvalue is unlikely to be different for this group, compared to the non-dormitory clusters and unlinked cases, the only alternative explanation is a higher value of r (=2.2).