Forecasting the scale of the COVID-19 epidemic in Kenya

AbstractBackgroundThe first COVID-19 case in Kenya was confirmed on March 13th, 2020. Here, we provide forecasts for the potential incidence rate, and magnitude, of a COVID-19 epidemic in Kenya based on the observed growth rate and age distribution of confirmed COVID-19 cases observed in China, whilst accounting for the demographic and geographic dissimilarities between China and Kenya.MethodsWe developed a modelling framework to simulate SARS-CoV-2 transmission in Kenya, KenyaCoV. KenyaCoV was used to simulate SARS-CoV-2 transmission both within, and between, different Kenyan regions and age groups. KenyaCoV was parameterized using a combination of human mobility data between the defined regions, the recent 2019 Kenyan census, and estimates of age group social interaction rates specific to Kenya. Key epidemiological characteristics such as the basic reproductive number and the age-specific rate of developing COVID-19 symptoms after infection with SARS-CoV-2, were adapted for the Kenyan setting from a combination of published estimates and analysis of the age distribution of cases observed in the Chinese outbreak.ResultsWe find that if person-to-person transmission becomes established within Kenya, identifying the role of subclinical, and therefore largely undetected, infected individuals is critical to predicting and containing a very significant epidemic. Depending on the transmission scenario our reproductive number estimates for Kenya range from 1.78 (95% CI 1.44 −2.14) to 3.46 (95% CI 2.81-4.17). In scenarios where asymptomatic infected individuals are transmitting significantly, we expect a rapidly growing epidemic which cannot be contained only by case isolation. In these scenarios, there is potential for a very high percentage of the population becoming infected (median estimates: >80% over six months), and a significant epidemic of symptomatic COVID-19 cases. Exceptional social distancing measures can slow transmission, flattening the epidemic curve, but the risk of epidemic rebound after lifting restrictions is predicted to be high.

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Differential effects of intervention timing on COVID-19 spread in the United States

Science Advances ◽

10.1126/sciadv.abd6370 ◽

2020 ◽

Vol 6 (49) ◽

pp. eabd6370 ◽

Cited By ~ 4

Author(s):

Sen Pei ◽

Sasikiran Kandula ◽

Jeffrey Shaman

Keyword(s):

United States ◽

Disease Transmission ◽

Human Mobility ◽

The United States ◽

Control Measures ◽

Basic Reproductive Number ◽

Transmission Model ◽

Reproductive Number ◽

Mobility Data ◽

Nonpharmaceutical Interventions

Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic.

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Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021

Journal of Urban Health ◽

10.1007/s11524-021-00566-7 ◽

2021 ◽

Author(s):

Shuhei Nomura ◽

Yuta Tanoue ◽

Daisuke Yoneoka ◽

Stuart Gilmour ◽

Takayuki Kawashima ◽

...

Keyword(s):

Care Delivery ◽

Human Mobility ◽

Age Groups ◽

Virus Transmission ◽

Initial Response ◽

Health Care Delivery System ◽

Population Characteristics ◽

Mobility Patterns ◽

Mobility Data ◽

The Status

AbstractIn the COVID-19 era, movement restrictions are crucial to slow virus transmission and have been implemented in most parts of the world, including Japan. To find new insights on human mobility and movement restrictions encouraged (but not forced) by the emergency declaration in Japan, we analyzed mobility data at 35 major stations and downtown areas in Japan—each defined as an area overlaid by several 125-meter grids—from September 1, 2019 to March 19, 2021. Data on the total number of unique individuals per hour passing through each area were obtained from Yahoo Japan Corporation (i.e., more than 13,500 data points for each area). We examined the temporal trend in the ratio of the rolling seven-day daily average of the total population to a baseline on January 16, 2020, by ten-year age groups in five time frames. We demonstrated that the degree and trend of mobility decline after the declaration of a state of emergency varies across age groups and even at the subregional level. We demonstrated that monitoring dynamic geographic and temporal mobility information stratified by detailed population characteristics can help guide not only exit strategies from an ongoing emergency declaration, but also initial response strategies before the next possible resurgence. Combining such detailed data with data on vaccination coverage and COVID-19 incidence (including the status of the health care delivery system) can help governments and local authorities develop community-specific mobility restriction policies. This could include strengthening incentives to stay home and raising awareness of cognitive errors that weaken people's resolve to refrain from nonessential movement.

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Adaptive data-driven age and patch mixing in contact networks with recurrent mobility

10.1101/2021.09.29.21264319 ◽

2021 ◽

Author(s):

Jesse Knight ◽

Huiting Ma ◽

Amir Ghasemi ◽

Mackenzie Hamilton ◽

Kevin Brown ◽

...

Keyword(s):

Disease Transmission ◽

Age Distribution ◽

Age Groups ◽

Data Driven ◽

Infectious Disease Transmission ◽

Mobility Data ◽

Contact Patterns ◽

Different Types ◽

Mixing Patterns ◽

Transmission Models

AbstractInfectious disease transmission models often stratify populations by age and geographic patches. Contact patterns between age groups and patches are key parameters in such models. Arenas et al. (2020) develop an approach to simulate contact patterns associated with recurrent mobility between patches, such as due to work, school, and other regular travel. Using their approach, mixing between patches is greater than mobility data alone would suggest, because individuals from patches A and B can form a contact if they meet in patch C. We build upon their approach to address three potential gaps that remain. First, our approach includes a distribution of contacts by age that is responsive to underlying age distribution of the mixing pool. Second, different age distributions by contact type are also maintained in our approach, such that changes to the numbers of different types of contacts are appropriately reflected in changes to the overall age mixing patterns. Finally, we introduce and distinguish between two mixing pools associated with each patch, with possible implications for the overall connectivity of the population: the home pool, in which contacts can only be formed with other individuals residing in the same patch; and the travel pool, in which contacts can be formed with some residents of, and any other visitors to the patch. We describe in detail the steps required to implement our approach, and present results of an example application.Graphical Abstract

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Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China

10.1101/2020.03.03.20030593 ◽

2020 ◽

Cited By ~ 68

Author(s):

Chaolong Wang ◽

Li Liu ◽

Xingjie Hao ◽

Huan Guo ◽

Qi Wang ◽

...

Keyword(s):

Attack Rate ◽

Healthcare Workers ◽

Age Groups ◽

Credible Interval ◽

Reproductive Number ◽

Demographic Groups ◽

Individual Level ◽

Epidemiological Characteristics ◽

The Impact ◽

Pharmaceutical Interventions

ABSTRACTBACKGROUNDWe described the epidemiological features of the coronavirus disease 2019 (Covid-19) outbreak, and evaluated the impact of non-pharmaceutical interventions on the epidemic in Wuhan, China.METHODSIndividual-level data on 25,961 laboratory-confirmed Covid-19 cases reported through February 18, 2020 were extracted from the municipal Notifiable Disease Report System. Based on key events and interventions, we divided the epidemic into four periods: before January 11, January 11-22, January 23 - February 1, and February 2-18. We compared epidemiological characteristics across periods and different demographic groups. We developed a susceptible-exposed-infectious-recovered model to study the epidemic and evaluate the impact of interventions.RESULTSThe median age of the cases was 57 years and 50.3% were women. The attack rate peaked in the third period and substantially declined afterwards across geographic regions, sex and age groups, except for children (age <20) whose attack rate continued to increase. Healthcare workers and elderly people had higher attack rates and severity risk increased with age. The effective reproductive number dropped from 3.86 (95% credible interval 3.74 to 3.97) before interventions to 0.32 (0.28 to 0.37) post interventions. The interventions were estimated to prevent 94.5% (93.7 to 95.2%) infections till February 18. We found that at least 59% of infected cases were unascertained in Wuhan, potentially including asymptomatic and mild-symptomatic cases.CONCLUSIONSConsiderable countermeasures have effectively controlled the Covid-19 outbreak in Wuhan. Special efforts are needed to protect vulnerable populations, including healthcare workers, elderly and children. Estimation of unascertained cases has important implications on continuing surveillance and interventions.

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An artificially simulated outbreak of a respiratory infectious disease

10.21203/rs.2.14142/v7 ◽

2020 ◽

Author(s):

Zuiyuan Guo ◽

Shuang Xu ◽

Libo Tong ◽

Botao Dai ◽

Yuandong Liu ◽

...

Keyword(s):

Infectious Disease ◽

Infectious Diseases ◽

Attack Rate ◽

General Pattern ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Developmental Trend ◽

Collision Model ◽

Military Camp ◽

Epidemiological Characteristics

Abstract Background Outbreaks of respiratory infectious diseases often occur in crowded places. To understand the pattern of spread of an outbreak of a respiratory infectious disease and provide a theoretical basis for targeted implementation of scientific prevention and control, we attempted to establish a stochastic model to simulate an outbreak of a respiratory infectious disease at a military camp. This model fits the general pattern of disease transmission and further enriches theories on the transmission dynamics of infectious diseases. Methods We established an enclosed system of 500 people exposed to adenovirus type 7 (ADV 7) in a military camp. During the infection period, the patients transmitted the virus randomly to susceptible people. The spread of the epidemic under militarized management mode was simulated using a computer model named “the random collision model”, and the effects of factors such as the basic reproductive number ( R 0 ), time of isolation of the patients (TOI), interval between onset and isolation (IOI), and immunization rates (IR) on the developmental trend of the epidemic were quantitatively analysed. Results Once the R 0 exceeded 1.5, the median attack rate increased sharply; when R 0 =3, with a delay in the TOI, the attack rate increased gradually and eventually remained stable. When the IOI exceeded 2.3 days, the median attack rate also increased dramatically. When the IR exceeded 0.5, the median attack rate approached zero. The median generation time was 8.26 days, (95% confidence interval [CI]: 7.84-8.69 days). The partial rank correlation coefficients between the attack rate of the epidemic and R 0 , TOI, IOI, and IR were 0.61, 0.17, 0.45, and -0.27, respectively. Conclusions The random collision model not only simulates how an epidemic spreads with superior precision but also allows greater flexibility in setting the activities of the exposure population and different types of infectious diseases, which is conducive to furthering exploration of the epidemiological characteristics of epidemic outbreaks.

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A mathematical model of COVID-19 transmission between frontliners and the general public

10.1101/2020.03.27.20045195 ◽

2020 ◽

Cited By ~ 1

Author(s):

Christian Alvin H. Buhat ◽

Monica C. Torres ◽

Yancee H. Olave ◽

Maica Krizna A. Gavina ◽

Edd Francis O. Felix ◽

...

Keyword(s):

Mathematical Model ◽

Customer Service ◽

General Public ◽

Food Service ◽

The Philippines ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Primary Case ◽

Epidemic Curve ◽

Community Effort

ABSTRACTThe number of COVID-19 cases is continuously increasing in different countries (as of March 2020) including the Philippines. It is estimated that the basic reproductive number of COVID-19 is around 1.5 to 4. The basic reproductive number characterizes the average number of persons that a primary case can directly infect in a population full of susceptible individuals. However, there can be superspreaders that can infect more than this estimated basic reproductive number. In this study, we formulate a conceptual mathematical model on the transmission dynamics of COVID-19 between the frontliners and the general public. We assume that the general public has a reproductive number between 1.5 to 4, and frontliners (e.g. healthcare workers, customer service and retail personnel, food service crews, and transport or delivery workers) have a higher reproduction number. Our simulations show that both the frontliners and the general public should be protected or resilient against the disease. Protecting only the frontliners will not result in flattening the epidemic curve. Protecting only the general public may flatten the epidemic curve but the infection risk faced by the frontliners is still high, which may eventually affect their work. Our simple model does not consider all factors involved in COVID-19 transmission in a community, but the insights from our model results remind us of the importance of community effort in controlling the transmission of the disease. All in all, the take-home message is that everyone in the community, whether a frontliner or not, should be protected or should implement preventive measures to avoid being infected.

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An artificially simulated outbreak of a respiratory infectious disease

10.21203/rs.2.14142/v5 ◽

2020 ◽

Author(s):

Zuiyuan Guo ◽

Shuang Xu ◽

Libo Tong ◽

Botao Dai ◽

Yuandong Liu ◽

...

Keyword(s):

Infectious Disease ◽

Infectious Diseases ◽

Attack Rate ◽

General Pattern ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Developmental Trend ◽

Collision Model ◽

Military Camp ◽

Epidemiological Characteristics

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Epidemic Dynamics and Patterns of Plant Diseases

Phytopathology ◽

10.1094/phyto.2001.91.10.1001 ◽

2001 ◽

Vol 91 (10) ◽

pp. 1001-1010 ◽

Cited By ~ 68

Author(s):

J. Segarra ◽

M. J. Jeger ◽

F. van den Bosch

Keyword(s):

Plant Diseases ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Disease Rate ◽

Epidemic Threshold ◽

Differential Delay Equation ◽

Differential Delay ◽

Epidemic Curve ◽

Epidemic Dynamics ◽

Special Cases

The general Kermack and McKendrick epidemic model (K&M) is derived with an appropriate terminology for plant diseases. The epidemic dynamics and patterns of special cases of the K&M model, such as the Vanderplank differential-delay equation; the compartmental healthy (H), latent (L), infectious (S), and postinfectious (R) model; and the K&M model with a delay-gamma-distributed sporulation curve were compared. The characteristics of the disease cycle are summarized by the basic reproductive number, R0, and the normalized sporulation curve, i(τ). We show how R0 and the normalized sporulation curve can be calculated from data in the literature. There are equivalences in the values of the basic reproductive number, R0, the epidemic threshold, and the final disease level across the different models.However, they differ in expressions for the initial disease rate, r, and the initial infection, Q, because the values depend on the sporulation curve. Expressions for r and Q were obtained for each model and can be used to approximate the epidemic curve by the logistic equation.

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COVID-19 Dynamics: A Heterogeneous Model

Frontiers in Public Health ◽

10.3389/fpubh.2020.558368 ◽

2021 ◽

Vol 8 ◽

Author(s):

Andrey Gerasimov ◽

Georgy Lebedev ◽

Mikhail Lebedev ◽

Irina Semenycheva

Keyword(s):

Early Stage ◽

Age Groups ◽

Population Heterogeneity ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Heterogeneous Model ◽

Recent Developments ◽

Homogeneous Models ◽

The Mathematical Model ◽

Stage Infection

The mathematical model reported here describes the dynamics of the ongoing coronavirus disease 2019 (COVID-19) epidemic, which is different in many aspects from the previous severe acute respiratory syndrome (SARS) epidemic. We developed this model when the COVID-19 epidemic was at its early phase. We reasoned that, with our model, the effects of different measures could be assessed for infection control. Unlike the homogeneous models, our model accounts for human population heterogeneity, where subpopulations (e.g., age groups) have different infection risks. The heterogeneous model estimates several characteristics of the epidemic more accurately compared to the homogeneous models. According to our analysis, the total number of infections and their peak number are lower compared to the assessment with the homogeneous models. Furthermore, the early-stage infection increase is little changed when population heterogeneity is considered, whereas the late-stage infection decrease slows. The model predicts that the anti-epidemic measures, like the ones undertaken in China and the rest of the world, decrease the basic reproductive number but do not result in the development of a sufficient collective immunity, which poses a risk of a second wave. More recent developments confirmed our conclusion that the epidemic has a high likelihood to restart after the quarantine measures are lifted.

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Bayesian approach for modelling the dynamic of COVID-19 outbreak on the Diamond Princess Cruise Ship

10.1101/2020.06.21.20136465 ◽

2020 ◽

Author(s):

Chao-Chih Lai ◽

Chen-Yang Hsu ◽

Hsiao-Hsuan Jen ◽

Amy Ming-Fang Yen ◽

Chang-Chuan Chan ◽

...

Keyword(s):

Population Size ◽

Large Population ◽

Credible Interval ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Cruise Ship ◽

Epidemic Curve ◽

Cruise Ships ◽

Epidemic Period ◽

Containment Measures

AbstractThe outbreaks of acute respiratory infectious disease with high attack rates on cruise ships were rarely studied. The outbreak of COVID-19 on the Diamond Princess Cruise Ship provides an unprecedented opportunity to estimate its original transmissibility with basic reproductive number (R0) and the effectiveness of containment measures. The traditional deterministic approach for estimating R0 is based on the outbreak of a large population size rather than that a small cohort of cruise ship. The parameters are therefore fraught with uncertainty. To tackle this problem, we developed a Bayesian Susceptible-Exposed-Infected-Recovery (SEIR) model with ordinary differential equation (ODE) to estimate three parameters, including transmission coefficients, the latent period, and the recovery rate given the uncertainty implicated the outbreak of COVID-19 on cruise ship with modest population size. Based on the estimated results on these three parameters before the introduction of partial containment measures, the natural epidemic curve after intervention was predicted and compared with the observed curve in order to assess the efficacy of containment measures. With the application of the Bayesian model to the empirical data on COVID-19 outbreak on the Diamond Princess Cruise Ship, the R0 was estimated as high as 5.71(95% credible interval: 4.08-7.55) because of its aerosols and fomite transmission mode. The simulated trajectory shows the entire epidemic period without containment measurements was approximately 47 days and reached the peak one month later after the index case. The partial containment measure reduced 34% (95% credible interval: 31-36%) infected passengers. Such a discovery provides an insight into timely evacuation and early isolation and quarantine with decontamination for containing other cruise ships and warship outbreaks.

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