Epidemiology and transmission of COVID-19 in cases and close contacts in Georgia in the first four months of the epidemic

BackgroundBetween February and June 2020, 917 COVID-19 cases and 14 COVID-19-related deaths were reported in Georgia. Early on, Georgia implemented non-pharmaceutical interventions (NPI) including extensive contact tracing and restrictions on movement. AimTo characterize the demographics of those tested and infected with COVID-19 in Georgia; to evaluate factors associated with transmission between cases and their contacts; and to determine how transmission varied due to NPI up to 24 June 2020. MethodsWe use data gathered by the Georgian National Center for Disease Control on all polymerase chain reaction tests conducted (among symptomatic patients, through routine testing and contact tracing); hospitalization data for confirmed cases, and contact tracing data. We calculated the number of contacts per index case, the secondary attack rate (% contacts infected), and effective R number (new cases per index case), and used logistic regression to estimate how age, gender, and contact type affected transmission. ResultsMost contacts and transmission events were between family members. Contacts <40 years were less likely to be infected, while infected individuals >50 were more likely to die than younger patients. Contact tracing identified 917 index cases with mean 3.1 contacts tested per case, primarily family members. The overall secondary attack rate was 28% (95% confidence interval [CI]: 26-29%) and effective R number was 0.87 (95%CI 0.81-0.93), peaking at 1.1 (95%CI 0.98-1.2) during the period with strongest restrictions. ConclusionGeorgia effectively controlled the COVID-19 epidemic in its early stages, although evidence does not suggest transmission was reduced during the strict lockdown period. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and MedRxiv for papers reporting research using contact tracing data to evaluate the characteristics of the COVID-19 epidemic in any country. A number of analyses were identified from Asia, including China, Taiwan, Maldives, Thailand, South Korea, and India, but none from other regions other than one previous analysis conducted in Europe, focusing on the first two months of the COVID-19 epidemic in Cyprus. Studies evaluated number of contacts and different contact types, secondary attack rate, and effective R number. However, none of these studies compared characteristics between different time periods or under varied levels of non-pharmaceutical interventions or restrictions on social mixing. Added value of this studyIn this study, we use contact tracing data from Georgia from all cases identified in the first four months of the epidemic, as well as testing and hospitalization data, to evaluate the number and type of contacts, effective R number (new cases per index case), and secondary attack rate (proportion of contacts infected) in this population, and whether these measures changed before, during, and after the lockdown period. We also evaluated how the chance of transmission varied by type of index case and contact. Our results indicate that number of contacts remained relatively low throughout the study period, so although the secondary attack rate was relatively high (28%) compared to that seen in studies in Asia (10-15%), the effective R number was less than one overall, peaking at 1.1 (0.98-1.2) during the strictest lockdown period, with easing of restrictions corresponding to a lower effective R of 0.87 (0.77-0.97). Most transmission occurred between family members with transmission very low between co-workers, friends, neighbours, and medical personnel, indicating that the restrictions on social mixing were effective at keeping the epidemic under control during this period. Implications of all the available evidenceOur study presents the first analysis of the successful control of a COVID-19 epidemic in a European country, indicating that despite a high secondary attack rate, reduction in contacts outside the home, and a well-timed lockdown, were able to keep transmission under control.

Comparison of Healthcare costs and benefits of the UK's Covid-19 response with four European countries: Decision Modelling Study

BackgroundIn responding to covid-19, governments have tried to balance protecting health while minimising Gross Domestic Product (GDP) losses. We compare health-related net benefit (HRNB) and GDP losses associated with government responses of the UK, Ireland, Germany, Spain, and Sweden from UK healthcare payer perspective. MethodsWe compared observed cases, hospitalisations, and deaths under "mitigation" to modelled events under "no mitigation" to 20th July 2020. We thus calculated healthcare costs, quality adjusted life years (QALYs), and HRNB at {pound}20,000/QALY saved by each country. On per population (i.e. per capita) basis, we compared HRNB with forecast reductions in 2020 GDP growth (overall or compared to Sweden as minimal mitigation country) and qualitatively and quantitatively described government responses. ResultsThe UK saved 3.17 (0.32-3.65) million QALYs, {pound}33 (8-38) billion healthcare costs, and {pound}1416 (220-1637) HRNB per capita at {pound}20,000/QALY. Per capita, this is comparable to {pound}1,455 GDP loss using Sweden as comparator and offsets 46.1 (7.1-53.2)% of total {pound}3075 GDP loss. Germany, Spain, and Sweden had greater HRNB per capita. These also offset a greater percentage of total GDP losses per capita. Ireland fared worst on both measures. Countries with more mask wearing, testing, and population susceptibility had better outcomes. Highest stringency responses did not appear to have best outcomes. ConclusionsOur exploratory analysis indicates the benefit of government covid-19 responses may outweigh their economic costs. The extent that HRNB offset economic losses appears to relate to population characteristics, testing levels, and mask wearing, rather than response stringency. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSOur research question was how the health-related net benefits and economic impacts of the UK response to the covid-19 epidemic first wave compared to other European countries. We searched PubMed, MedRxiv, and Arxiv for terms related to cost-effectivness, covid-19, and the UK. Two studies compared predicted lives saved to predicted gross domestic product (GDP) losses. One found that lives saved by a lockdown would outweigh GDP losses, while another found a lockdown to cost {pound}10million per life saved. A later modelling study used quality adjusted life-years (QALYs), going beyond lives saved, and found cost per QALY was below {pound}50,000. A fourth, comparing observed to modelled deaths and hospitalisations, found the cost per QALY was at least {pound}220,000, and thus the UK response was not cost-effective. None of these were international comparisons. One international study found good health and economic outcomes to be correlated. Another found global trade reductions and voluntary behavioural changes to have greater impact on economic growth than government measures. Neither considered cost-effectiveness. However, they suggest comparison to GDP loss is naive as this is total loss and not that due to government restrictions. Added value of this studyWe compare the UK to Ireland, Germany, Spain, and Sweden on health-related net benefits and economic impacts of government response from a UK National Health Service (NHS) perspective. We describe countries response measures. We compared model predictions of outcomes under "no mitigation" to observed outcomes under "mitigation" up to July 20th 2020. We estimated healthcare costs, QALYs, and health-related net benefit (HRNB) saved. We compared HRBN to GDP losses using Sweden as a "minimal mitigation" comparator and calculated the % of total GDP loss they offset. We found the UK saved 3{middle dot}17 (0{middle dot}32-3{middle dot}65) million QALYs, {pound}33 ({pound}8-38) billion in healthcare costs, and {pound}1416 (220-1637) HRNB per capita at the NHS threshold of {pound}20,000/QALY. This is comparable to the {pound}1,455 GDP loss per capita using Sweden as comparator and offsets 46{middle dot}1% (7{middle dot}1-53{middle dot}2) of the total estimated {pound}3075 GDP loss per capita. We found that Germany, Spain, and probably Sweden had greater HRNB per capita and offset greater percentages of GDP loss per capita. Ireland fared worst on both measures. We found countries with more mask wearing, testing, and population susceptibility (e.g. older and more interpersonal contact) had better outcomes. Highest stringency responses did not appear to have best outcomes. Implications of all the available evidenceWe add to growing evidence that the total economic impact of covid-19 exceeds the HRNB of the UKs response. However, using Sweden as comparator and comparing across countries, we argue that GDP loss is not purely due to government restrictions and that due to restrictions may be outweighed by HRNB. We evaluated the extent to which countries have offset GDP losses, and these appear to be higher in countries with more at-risk populations, higher testing, and higher mask wearing, rather than those with most stringent restrictions.

Estimating the impact of disruptions due to COVID-19 on HIV transmission and control among men who have sex with men in China

IntroductionThe COVID-19 pandemic is impacting HIV care globally, with gaps in HIV treatment expected to increase HIV transmission and HIV-related mortality. We estimated how COVID-19-related disruptions could impact HIV transmission and mortality among men who have sex with men (MSM) in four cities in China. MethodsRegional data from China indicated that the number of MSM undergoing facility-based HIV testing reduced by 59% during the COVID-19 pandemic, alongside reductions in ART initiation (34%), numbers of sexual partners (62%) and consistency of condom use (25%). A deterministic mathematical model of HIV transmission and treatment among MSM in China was used to estimate the impact of these disruptions on the number of new HIV infections and HIV-related deaths. Disruption scenarios were assessed for their individual and combined impact over 1 and 5 years for a 3-, 4- or 6-month disruption period. ResultsOur China model predicted that new HIV infections and HIV-related deaths would be increased most by disruptions to viral suppression, with 25% reductions for a 3-month period increasing HIV infections by 5-14% over 1 year and deaths by 7-12%. Observed reductions in condom use increased HIV infections by 5-14% but had minimal impact (<1%) on deaths. Smaller impacts on infections and deaths (<3%) were seen for disruptions to facility testing and ART initiation, but reduced partner numbers resulted in 11-23% fewer infections and 0.4-1.0% fewer deaths. Longer disruption periods of 4 and 6 months amplified the impact of combined disruption scenarios. When all realistic disruptions were modelled simultaneously, an overall decrease in new HIV infections was always predicted over one year (3-17%), but not over 5 years (1% increase-4% decrease), while deaths mostly increased over one year (1-2%) and 5 years (1.2 increase - 0.3 decrease). ConclusionsThe overall impact of COVID-19 on new HIV infections and HIV-related deaths is dependent on the nature, scale and length of the various disruptions. Resources should be directed to ensuring levels of viral suppression and condom use are maintained to mitigate any adverse effects of COVID-19 related disruption on HIV transmission and control among MSM in China.