Department of Software and Information Systems Engineering, Ben Gurion University; Beer Sheva, Israel.
Department of Information Systems, University of Haifa; Haifa, Israel.
PLoS One. 2023 Jan 26;18(1):e0280874. doi: 10.1371/journal.pone.0280874. eCollection 2023.
Epidemics and pandemics require an early estimate of the cumulative infection prevalence, sometimes referred to as the infection "Iceberg," whose tip are the known cases. Accurate early estimates support better disease monitoring, more accurate estimation of infection fatality rate, and an assessment of the risks from asymptomatic individuals. We find the Pivot group, the population sub-group with the highest probability of being detected and confirmed as positively infected. We differentiate infection susceptibility, assumed to be almost uniform across all population sub-groups at this early stage, from the probability of being confirmed positive. The latter is often related to the likelihood of developing symptoms and complications, which differs between sub-groups (e.g., by age, in the case of the COVID-19 pandemic). A key assumption in our method is the almost-random subgroup infection assumption: The risk of initial infection is either almost uniform across all population sub-groups or not higher in the Pivot sub-group. We then present an algorithm that, using the lift value of the pivot sub-group, finds a lower bound for the cumulative infection prevalence in the population, that is, gives a lower bound on the size of the entire infection "Iceberg." We demonstrate our method by applying it to the case of the COVID-19 pandemic. We use UK and Spain serological surveys of COVID-19 in its first year to demonstrate that the data are consistent with our key assumption, at least for the chosen pivot sub-group. Overall, we applied our methods to nine countries or large regions whose data, mainly during the early COVID-19 pandemic phase, were available: Spain, the UK at two different time points, New York State, New York City, Italy, Norway, Sweden, Belgium, and Israel. We established an estimate of the lower bound of the cumulative infection prevalence for each of them. We have also computed the corresponding upper bounds on the infection fatality rates in each country or region. Using our methodology, we have demonstrated that estimating a lower bound for an epidemic's infection prevalence at its early phase is feasible and that the assumptions underlying that estimate are valid. Our methodology is especially helpful when serological data are not yet available to gain an initial assessment on the prevalence scale, and more so for pandemics with an asymptomatic transmission, as is the case with Covid-19.
疫情和大流行需要对累计感染率进行早期估计，有时也被称为感染的“冰山一角”，其尖端是已知的病例。准确的早期估计有助于更好地监测疾病，更准确地估计感染死亡率，并评估无症状个体的风险。我们找到了 Pivot 组，这是最有可能被发现和确认为阳性感染的人群亚组。我们区分了感染易感性，在早期阶段，假设所有人群亚组的感染易感性几乎相同，而与被确认为阳性的概率不同。后者通常与症状和并发症的发生概率有关，而这些概率在亚组之间是不同的（例如，在 COVID-19 大流行期间，按年龄划分）。我们的方法中的一个关键假设是几乎随机的亚组感染假设：初始感染的风险在所有人群亚组中几乎相同，或者在 Pivot 亚组中没有更高。然后，我们提出了一种算法，该算法使用 Pivot 亚组的提升值，找到了人群中累计感染率的下限，即整个感染“冰山一角”的大小下限。我们通过将其应用于 COVID-19 大流行的案例来演示我们的方法。我们使用英国和西班牙在 COVID-19 大流行第一年进行的血清学调查来证明，数据至少与我们选择的 Pivot 亚组的关键假设是一致的。总体而言，我们将我们的方法应用于九个国家或地区，这些国家或地区的数据（主要是在 COVID-19 大流行的早期阶段）可用：西班牙、英国在两个不同的时间点、纽约州、纽约市、意大利、挪威、瑞典、比利时和以色列。我们为它们中的每一个都估计了累计感染率下限。我们还计算了每个国家或地区感染死亡率的相应上限。使用我们的方法，我们已经证明，在疫情早期阶段估计疫情感染率的下限是可行的，并且该估计所依据的假设是有效的。当还没有血清学数据来获得流行规模的初步评估时，我们的方法特别有帮助，对于像 COVID-19 这样具有无症状传播的大流行更是如此。
