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Effect of changing case definitions for COVID-19 on the epidemic curve and transmission parameters in mainland China: a modelling study.

ABSTRACT: BACKGROUND:When a new infectious disease emerges, appropriate case definitions are important for clinical diagnosis and for public health surveillance. Tracking case numbers over time is important to establish the speed of spread and the effectiveness of interventions. We aimed to assess whether changes in case definitions affected inferences on the transmission dynamics of coronavirus disease 2019 (COVID-19) in China. METHODS:We examined changes in the case definition for COVID-19 in mainland China during the first epidemic wave. We used exponential growth models to estimate how changes in the case definitions affected the number of cases reported each day. We then inferred how the epidemic curve would have appeared if the same case definition had been used throughout the epidemic. FINDINGS:From Jan 15 to March 3, 2020, seven versions of the case definition for COVID-19 were issued by the National Health Commission in China. We estimated that when the case definitions were changed, the proportion of infections being detected as cases increased by 7·1 times (95% credible interval [CrI] 4·8-10·9) from version 1 to 2, 2·8 times (1·9-4·2) from version 2 to 4, and 4·2 times (2·6-7·3) from version 4 to 5. If the fifth version of the case definition had been applied throughout the outbreak with sufficient testing capacity, we estimated that by Feb 20, 2020, there would have been 232?000 (95% CrI 161?000-359?000) confirmed cases in China as opposed to the 55?508 confirmed cases reported. INTERPRETATION:The case definition was initially narrow and was gradually broadened to allow detection of more cases as knowledge increased, particularly milder cases and those without epidemiological links to Wuhan, China, or other known cases. These changes should be taken into account when making inferences on epidemic growth rates and doubling times, and therefore on the reproductive number, to avoid bias. FUNDING:Health and Medical Research Fund, Hong Kong.

SUBMITTER: Tsang TK

PROVIDER: S-EPMC7173814 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Effect of changing case definitions for COVID-19 on the epidemic curve and transmission parameters in mainland China: a modelling study.

Tsang Tim K TK Wu Peng P Lin Yun Y Lau Eric H Y EHY Leung Gabriel M GM Cowling Benjamin J BJ

The Lancet. Public health 20200421 5

<h4>Background</h4>When a new infectious disease emerges, appropriate case definitions are important for clinical diagnosis and for public health surveillance. Tracking case numbers over time is important to establish the speed of spread and the effectiveness of interventions. We aimed to assess whether changes in case definitions affected inferences on the transmission dynamics of coronavirus disease 2019 (COVID-19) in China.<h4>Methods</h4>We examined changes in the case definition for COVID-1 ...[more]

PMID: 32330458

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Project description:BackgroundSince the outbreak of COVID-19 in December 2019 in Wuhan, Hubei Province, China, frequent interregional contacts and the high rate of infection spread have catalyzed the formation of an epidemic network.ObjectiveThe aim of this study was to identify influential nodes and highlight the hidden structural properties of the COVID-19 epidemic network, which we believe is central to prevention and control of the epidemic.MethodsWe first constructed a network of the COVID-19 epidemic among 31 provinces in mainland China; after some basic characteristics were revealed by the degree distribution, the k-core decomposition method was employed to provide static and dynamic evidence to determine the influential nodes and hierarchical structure. We then exhibited the influence power of the above nodes and the evolution of this power.ResultsOnly a small fraction of the provinces studied showed relatively strong outward or inward epidemic transmission effects. The three provinces of Hubei, Beijing, and Guangzhou showed the highest out-degrees, and the three highest in-degrees were observed for the provinces of Beijing, Henan, and Liaoning. In terms of the hierarchical structure of the COVID-19 epidemic network over the whole period, more than half of the 31 provinces were located in the innermost core. Considering the correlation of the characteristics and coreness of each province, we identified some significant negative and positive factors. Specific to the dynamic transmission process of the COVID-19 epidemic, three provinces of Anhui, Beijing, and Guangdong always showed the highest coreness from the third to the sixth week; meanwhile, Hubei Province maintained the highest coreness until the fifth week and then suddenly dropped to the lowest in the sixth week. We also found that the out-strengths of the innermost nodes were greater than their in-strengths before January 27, 2020, at which point a reversal occurred.ConclusionsIncreasing our understanding of how epidemic networks form and function may help reduce the damaging effects of COVID-19 in China as well as in other countries and territories worldwide.

Project description:BackgroundSevere acute respiratory syndrome (SARS) originated in China in 2002, and it spread to 26 provinces in mainland China and 32 countries across five continents in a matter of months. This outbreak resulted in 774 deaths. However, the spatial features and potential determinants of SARS input-output flows remain unclear.MethodsWe used an adjusted spatial interaction model to examine the spatial effects and potential factors associated with SARS input-output flows.ResultsThe presence of origin-based spatial dependence positively affected SARS input-output flows from the neighbours of the origin regions. Two components of the input-output flows, migrant and hospitalization flows, exhibited distinctive features. The origin-based and destination-based spatial dependence positively affected migrant flows (i.e., due to those seeking jobs) from the neighbours of origin and destination locations. Similarly, the destination-based spatial dependence also positively affected hospitalization flows (i.e., due to those seeking treatment) from the neighbours of destination regions. However, the origin-to-destination based spatial dependence negatively affected hospitalisation flows from the neighbours of origin-to-destination regions. The direct effects accounted for 78% of the SARS input-output flows, which was 3.56-fold greater than the indirect effects. Differences in regional income drove the SARS input-output flows. Therefore, urban income had a positive effect, whereas rural income had a negative effect. Total interregional flows increased by 3.54% with a 1% increase in urban income, and intraregional flows increased by 8.35%. In contrast, the total interregional flows decreased by 3.38% with a 1% increase in rural income, and intraregional flows declined by 2.29%. Railway capacity, per person gross domestic product (PGDP), urban rate and the law of distance decay also affected the input-output flows.ConclusionsOur results confirm that the SARS input-output flows presented significant geographic spatial heterogeneity and spatial effects. Income differences were the major cause of the flows between pairs of regions. Railway capacity, PGDP, and urban rate also played important roles. These findings provide valuable information for the Chinese government to control the future spread of nationwide epidemics.

Project description:BackgroundMucormycosis is a lethal fungal infection with increasing incidence. The epidemiology of mucormycosis in current mainland China has not been fully elucidated.ObjectivesTo investigate the epidemiology, risk factors, manifestations, diagnosis, treatment and prognosis of mucormycosis in mainland China.MethodsWe searched for published mucormycosis case reports/series in mainland China in the PubMed, WanFang and China National Knowledge Infrastructure databases from January 2001 to July 2020. Cases of proven/probable mucormycosis were included.ResultsA total of 390 cases were included in this review. Most of the patients were male (61.3%), and diabetes was the most common predisposing factor (37.2%). Pulmonary mucormycosis (42.1%) was the most common form followed by cutaneous infection (21.0%). Of 390 patients, 24 died before therapy. Among the remaining 366 patients, 208 (56.8%) received antifungal drugs alone, 16 (4.4%) received surgery alone, and 142 (38.8%) received a combination of drugs and surgery, the mortality of the last group is much lower (34/142, 23.9%). The overall mortality was 37.2%. A multivariate analysis indicated that factors associated with increased mortality included corticosteroid use alone as immunosuppressive therapy, rhino-orbito-cerebral or disseminated mucormycosis (compared with pulmonary mucormycosis), and drug administration other than amphotericin B (AmB), posaconazole (POS) and itraconazole (ITR) (compared with the use of conventional AmB), while factors associated with decreased mortality included cutaneous mucormycosis and surgical therapy. Combination or sequential antifungal therapy of AmB and POS or ITR did not reduce mortality compared with conventional AmB monotherapy.ConclusionIn mainland China, mucormycosis is a serious fungal infection with high mortality. Corticosteroid use, rhino-orbito-cerebral and disseminated mucormycosis were adverse prognostic factors. Antifungal therapy combined with surgery could improve the prognosis.

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Effect of changing case definitions for COVID-19 on the epidemic curve and transmission parameters in mainland China: a modelling study.

Publications

Effect of changing case definitions for COVID-19 on the epidemic curve and transmission parameters in mainland China: a modelling study.

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