The impact of intervention to the incidence of natural focus diseases during the outbreak of COVID-19 in Jiangsu Province, China (preprint)

Doxorubicin (DOX) is a well-known chemotherapeutic drug for most malgnencies including breast cancer and leukemia whilst the usage of DOX is limited owing to its cardiotoxicity. The present study analyzed the effects of crocin on doxorubicin’s cardiotoxic efect in rat myocardium and searched their mechanistic interaction in the pathogenesis of DOX-induced myocardial toxicity. Forty rats were divided into four groups; (a) control (received normal saline as a dose of 1 ml/kg by ip for 15 days), (b) Crocin (received crocin as a dose of 40 mg/kg/24h by ip for 15 days), (c) DOX (received DOX as a dose of 2 mg/kg/48h by ip in six injection, cumulative dose 12 mg/kg), and (d) DOX+Crocin (received DOX as a dose of 2 mg/kg/48h by ip in six injection and crocin as a dose of 40 mg/kg/24h ip for 15 days). According to the present study, DOX administration caused significant increases in lipid indices (triglyseride, low-dencity lipoproteins and very low-dencity lipoproteins) as well as cardiac markers (Creatine kinase-muscle/brain and Cardiac Troponin I). Morever, DOX caused significant increases in oxidative stress parameters (malondialdehyde and total oxidant status) as well as decreases in antioxidant defense systems (glutathione, superoxide dismutase, catalase and total antioxidant status). The present study also demonstrated that co-administration of crocin with DOX significantly ameliorated the lipid profile and biochemical parameters in rats receiving DOX. The results were supported by histopathological and immunohistochemical evaluations. Taken together, our results reveal that crocin might be a cardioprotective agent in DOX treated patients for cancer.

Control Measures During the COVID-19 Outbreak Reduced the Transmission of Hand, Foot, and Mouth Disease (preprint)

Background: Control measures during the coronavirus disease 2019 (COVID-19) outbreak may have limited the spread of infectious diseases. This study aimed to analyse the impact of COVID-19 on the spread of hand, foot, and mouth disease (HFMD) in China. Methods: A mathematical model was established to fit the reported data of HFMD in six selected cities in mainland China from 2015 to 2020. The absolute difference (AD) and relative difference (RD) between the reported incidence in 2020, and simulated maximum, minimum, or median incidence of HFMD in 2015-2019 were calculated. Findings: The incidence and Reff of HFMD have decreased in six selected cities since the outbreak of COVID-19, and in the second half of 2020, the incidence and R eff of HFMD have rebounded. The results show that the total attack rate (TAR) in 2020 was lower than the maximum, minimum, and median TAR fitted in previous years in six selected cities (except Changsha city). For the maximum, median, minimum fitted TAR, the range of RD (%) is 42·20-99·20%, 36·35-98·41% 48·35-96·23% (except Changsha city) respectively. Interpretation: Based on the incidence data of six cities from 2015 to 2019, the SEIAR model demonstrated a significant effect on the incidence of HFMD. During the period of COVID-19, the incidence and R eff of HFMD decreased, the prevention and control measures taken during the period of COVID-19, such as school suspension, home quarantine, closing all kinds of leisure places, wearing masks, advocating frequent hand washing, etc., have not only effectively suppressed the spread of COVID-19 epidemic, but also have significantly contributed to the containment of HFMD transmission.Funding Statement: This study was partly supported by the Bill & Melinda Gates Foundation (INV-005834).Declaration of Interests: The authors declare no conflicts of interests.

The Impact of Intervention to Routine Surveillance of Natural Focal Diseases During the Outbreak of COVID-19 in Jiangsu Province, China (preprint)

Background: With the strength intervention of China, the outbreak of Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) had a great control effect. The measures may influence the development and progression of others infectious diseases.Method: The data of daily coronavirus virus disease 2019 (COVID-19) confirmed cases from January 3, 2020 to April 30, 2020 and natural focal disease cases from January, 2005 to April, 2020 were collected from Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Provincial CDC). We describe and compare the data of natural focal diseases from January to April, 2020 with the same months from 2015 to 2019 in the four aspects: trend of incidence, regional, age and sex distribution. Nonparametric tests were used to analyzed to the difference between the duration from onset of illness to date of diagnosis of natural focal diseases and the same period of the previous year. Results: The incidence of malaria in February (0.9 per 10,000,000 people), March (0.3 per 10,000,000 people) and April (0.1 per 10,000,000 people) 2020 less than the lower limit for range of February (1.6-4.5 per 10,000,000 people), March (0.8-3.3 per 10,000,000 people) and April (1.0-2.9 per 10,000,000 people) from 2015 to 2019 respectively. The incidence of brucellosis in February was 0.9 (per 10,000,000 people), less than the lower limit for the range from 2015 to 2019 (1.6-4.5 per 10,000,000 people). The incidence of hemorrhagic fever (HF) in March was 1.0 (per 10,000,000 people), less than the lower limit for the range from 2015 to 2019 (1.4-2.6 per 10,000,000 people). However, the incidence of Severe Fever with Thrombocytopenia Syndrome (SEFT) in March was 0.3 (per 10,000,000 people), higher than the upper limit for the range from 2015 to 2019 (0.0-0.1 per 10,000,000 people). Furthermore, we respectively observed the incidence with various degree of reduction in male, 20-60 years old and both rural and urban areas. Conclusions: In Jiangsu province, the incidence of natural focal diseases decreased during the outbreak of COVID-19 in 2020, especially malaria, HF and SEFT. The impact of interventions were felt most by male individuals within the age group of 20-50 years. The interventions for COVID-19 may control the epidemics of natural focal diseases.

Forecasting the Effectiveness of Interventions to Control the Second Wave of COVID-19 Outbreaks: A Modelling Study in 12 Countries of 6 Regions (preprint)

Background: In most countries, it is hard to effectively control coronavirus disease 2019 (COVID-19). This study conducted the most comprehensive evaluation of the effects of pharmacological (like vaccination, pharmacotherapy ) and non-pharmacological (like isolation, social distancing and mask-wearing) interventions taken singly or in combination for the first time globally.Methods: We estimate that across these 12 countries that are different but presentative, interventions prevented or delayed roughly millions of confirmed cases. This study constructs mathematical model, which interventions includes vaccination, pharmacotherapy, isolation, social distancing and mask-wearing , and analyses the effect of these interventions used alone and in combination.Findings: The basic reproduction number (R0) of each country mostly range from 3 to 5. In terms of the effect of single intervention, for countries such as China, South Korea, Thailand, US, South Africa and Algeria, it is preferred to recommend these countries to adopt isolation to prevent and control the second wave of COVID-19 outbreak, while for countries such as Russia, UK, Saudi Arabia, India and Brazil, wearing masks is the best choice. Especially pharmacotherapy can play a good role in Iran. When combinations with different interventions were taken, the situation was different. For US, Brazil and Algeria, the combination of “Vaccination & Isolation & Wearing mask” is recommended in these countries to prevent and control the development of COVID-19, and the combination of “Isolation & Social distancing & Wearing mask” is recommended in UK and China. For the rest, we suggest that Russia, Iran, Saudi Arabia, India, Thailand and South Africa take the intervention measures of “Vaccination & Medical treatment & Isolation & Wearing mask”, “Vaccination & Medical treatment”, “Vaccination & Social distancing & Wearing mask”, “Medical treatment & Social distancing & Wearing mask”, “Vaccination & Medical Treatment & Isolation”, “Vaccination & Medical Treatment & Wearing mask”, respectively to deal with the second wave of outbreaks that may come by the end of this year.Interpretation: Our model is operable and selective for the prevention and control of epidemic situations in various countries. These findings may help policy makers in the 180+ countries where COVID-19 has been reported around the world to identify the most effective and socioeconomically acceptable measures to prevent and control the second wave of COVID-19 epidemic, and inform if when these policies should be deployed, intensified or replaced.Funding: This study was partly supported by the Bill & Melinda Gates Foundation (INV-005834), the Science and Technology Program of Fujian Province (No: 2020Y0002), the Xiamen New Coronavirus Prevention and Control Emergency Tackling Special Topic Program (No: 3502Z2020YJ03), and the Open Research Fund of State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics (SKLVD2019KF005).Declaration of Interests: The authors declare no competing interests.

Effectiveness of Pharmacological Interventions to Control COVID-19: An Age-Specific Modelling Study (preprint)

Background: Novel coronavirus disease 2019 (COVID-19) causes an immense disease burden. Only drugs or vaccines can eliminate the virus. Methods: We adopted our age-specific transmission model by susceptible-exposed-infectious -critically ill-asymptomatic-removed (SEICAR) model. Effects of different drug types were simulated by changing transmission rate (β), critical case fatality rate (fc), and disease duration of each age group. Evaluation indexes were based on outbreak duration(OD), cumulative number of cases(CNC), total attack rate(TAR), peak date(PD), number of peak cases(NPC), and case fatality rate(f). Findings: When without intervention, changing in β and disease duration, as the age increased, OD decreased, TAR increased, PD advanced, CCN and NPC initially increased and then decreased, while f decreased first and then increased. When disease duration and β remained unchanged, changing fc did not affect the epidemic. All age groups had 40% shorter disease duration but unchanged fc, while β was reduced by 60%, which reduced TAR of group 1 (≤14 years) from 2·35% to 0·09%; f of group 4 (≥65 years) was reduced from 1·04% to 0·05%. Interpretation: Drugs had different age-dependent effects. If a drug can control the disease duration or β of all age groups, younger people would have the fastest transmission control and seniors will have the best improvement in disease severity. Funding: The Bill & Melinda Gates Foundation (INV-005834); the Science and Technology Program of Fujian Province (No: 2020Y0002), and the Xiamen New Coronavirus Prevention and Control Emergency Tackling Special Topic Program (No: 3502Z2020YJ03).Declaration of Interests: The authors declare no competing interests.

The relationship between key natural and social factors and the transmission of novel coronavirus disease 2019 in China (preprint)

Background Novel coronavirus disease 2019 (COVID-19) has become a global pandemic. This study aims to explore the relationship between key natural and social factors and the transmission of COVID-19 in China.Methods This study collected the number of confirmed cases of COVID-19 in 21 provinces and cities in China as of February 28, 2020. Three provinces were included in the sample: Hainan, Guizhou, and Qinghai. The 18 cities included Shanghai, Tianjin and so on. Key natural factors comprised monthly average temperatures in the January and February 2020 and spatial location as determined by longitude and latitude. Social factors were population density, Gross Domestic Product (GDP), number of medical institutions and health practitioners; as well as the per capita values for GDP, medical institutions, and health practitioners. Excel was used to collate the data and draw the temporal and spatial distribution map of the prevalence rate (PR) and the proportion of local infection (PLI). The influencing factors were analyzed by SPSS 21.0 statistical software, and the relationship between the dependent and independent variables was simulated by 11 models. Finally, we choose the exponential model according to the value of R2 and the applicability of the model.Results The temporal and spatial distribution of the PR varies across the 21 provinces and cities identified. The PR generally decreases with distance from Hubei, except in the case of Shenzhen City, where the converse is observed. The results of the exponential model simulation show that the monthly minimum, median, and maximum average temperatures in January and February, and the latitude and population density are significant and thus will affect the PLI. The corresponding values of R2 are 0.297, 0.322, 0.349, 0.290, 0.314, 0.339, 0.344, and 0.301. The effects of other factors were not statistically significant.Conclusions Among the selected key natural and social factors, higher temperatures may decrease the transmission of COVID-19. From this analysis, it is evident that if the temperature decreases by 1℃, the average PLI increases by 0.01. Further, it was established that locations at more northern latitudes had a higher PLI, and population density showed an inverse relationship with PLI.

A mathematical model for estimating the age-specific transmissibility of a novel coronavirus (preprint)

Background: A novel coronavirus named as "SARS-CoV-2" has spread widely in many countries since December 2019, especially in China. This study aimed to quantify the age-specific transmissibility by using a mathematical model. Methods: An age-specific susceptible - exposed - symptomatic - asymptomatic - recovered - seafood market (SEIARW) model was developed based on two suspected transmission routes (from market to person and person to person). The susceptible people from Wuhan City were divided into different age groups. We used the subscript i and j to represent age group 1 to 4 (1: <= 14 years; 2: 15-44 years; 3: 45-64 years; 4: >= 65 years) and 1 to 5 (1: <= 5 years; 2: 6-14 years; 3: 15-24 years; 4: 25-59 years; 4: >= 60 years), respectively. Data of reported COVID-19 cases were collected from one published literature from 26 November to 22 December, 2019 in Wuhan City, China. The age-specific transmissibility of the virus was estimated accordingly secondary attack rate (SAR). Results: The age-specific SEIARW model fitted with the reported data well by dividing the population into four age groups ({chi}2 = 4.99 x 10-6, P > 0.999), and five age groups ({chi}2 = 4.85 x 10-6, P > 0.999). Based on the four-age-group SEIARW model, the highest transmissibility occurred from age group 2 to 3 (SAR23 = 17.56 per 10 million persons), followed by from age group 3 to 2 (SAR32 = 10.17 per 10 million persons). The lowest transmissibility occurred from age group 1 to 2 (SAR12 = 0.002 per 10 million persons). Based on the five-age-group SEIARW model, the highest transmissibility occurred from age group 4 to 5 (SAR45 = 12.40 per 10 million persons), followed by from age group 5 to 4 (SAR54 = 6.61 per 10 million persons). The lowest transmissibility occurred from age group 3 to 4 (SAR34 = 0.0002 per 10 million persons). Conclusions: SARS-CoV-2 has high transmissibility among adults and elder people but low transmissibility among children and young people.