Publications
90 results found
Bhatia S, Imai N, Cuomo-Dannenburg G, et al., 2020, Report 6: Relative sensitivity of international surveillance, Report 6: Relative sensitivity of international surveillance
Since the start of the COVID-19 epidemic in late 2019, there are now 29 affected countries with over 1000 confirmed cases outside of mainland China. In previous reports, we estimated the likely epidemic size in Wuhan City based on air traffic volumes and the number of detected cases internationally. Here we analysed COVID-19 cases exported from mainland China to different regions and countries, comparing the country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different countries. Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that about two thirds of COVID-19 cases exported from mainland China have remained undetected worldwide, potentially resulting in multiple chains of as yet undetected human-to-human transmission outside mainland China.
Djaafara BA, Imai N, Hamblion E, et al., 2020, A quantitative framework to define the end of an outbreak: application to Ebola Virus Disease
<jats:p>Declaring the end of an outbreak is an important step in controlling infectious disease outbreaks. An objective estimation of the probability of cases arising in the future is important to reduce the risk of post-declaration flare-ups. We developed a simulation-based model to quantify that probability. We tested it on simulated Ebola Virus Disease (EVD) data and found this probability was most sensitive to the instantaneous reproduction number, the reporting rate, and the delay between symptom onset and recovery or death of the last detected case. For EVD, our results suggest that the current WHO criterion of 42 days since the outcome of the last detected case is too short and very sensitive to underreporting. The 90 days of enhanced surveillance period after the end-of-outbreak declaration is therefore crucial to capture potential flare-ups of cases. Hence, we suggest a shift to a preliminary end-of-outbreak declaration after 63 days from the symptom onset day of the last detected case. This should be followed by a 90-day enhanced surveillance, after which the official end-of-outbreak can be declared. This corresponds to less than 5% probability of flare ups in most of the scenarios examined. Our quantitative framework could be adapted to define end-of-outbreak criteria for other infectious diseases.</jats:p>
Volz E, Baguelin M, Bhatia S, et al., 2020, Report 5: Phylogenetic analysis of SARS-CoV-2
Genetic diversity of SARS-CoV-2 (formerly 2019-nCoV), the virus which causes COVID-19, provides information about epidemic origins and the rate of epidemic growth. By analysing 53 SARS-CoV-2 whole genome sequences collected up to February 3, 2020, we find a strong association between the time of sample collection and accumulation of genetic diversity. Bayesian and maximum likelihood phylogenetic methods indicate that the virus was introduced into the human population in early December and has an epidemic doubling time of approximately seven days. Phylodynamic modelling provides an estimate of epidemic size through time. Precise estimates of epidemic size are not possible with current genetic data, but our analyses indicate evidence of substantial heterogeneity in the number of secondary infections caused by each case, as indicated by a high level of over-dispersion in the reproduction number. Larger numbers of more systematically sampled sequences – particularly from across China – will allow phylogenetic estimates of epidemic size and growth rate to be substantially refined.
Dorigatti I, Okell L, Cori A, et al., 2020, Report 4: Severity of 2019-novel coronavirus (nCoV)
We present case fatality ratio (CFR) estimates for three strata of 2019-nCoV infections. For cases detected in Hubei, we estimate the CFR to be 18% (95% credible interval: 11%-81%). For cases detected in travellers outside mainland China, we obtain central estimates of the CFR in the range 1.2-5.6% depending on the statistical methods, with substantial uncertainty around these central values. Using estimates of underlying infection prevalence in Wuhan at the end of January derived from testing of passengers on repatriation flights to Japan and Germany, we adjusted the estimates of CFR from either the early epidemic in Hubei Province, or from cases reported outside mainland China, to obtain estimates of the overall CFR in all infections (asymptomatic or symptomatic) of approximately 1% (95% confidence interval 0.5%-4%). It is important to note that the differences in these estimates does not reflect underlying differences in disease severity between countries. CFRs seen in individual countries will vary depending on the sensitivity of different surveillance systems to detect cases of differing levels of severity and the clinical care offered to severely ill cases. All CFR estimates should be viewed cautiously at the current time as the sensitivity of surveillance of both deaths and cases in mainland China is unclear. Furthermore, all estimates rely on limited data on the typical time intervals from symptom onset to death or recovery which influences the CFR estimates.
Cattarino L, Rodriguez-Barraquer I, Imai N, et al., 2020, Mapping global variation in dengue transmission intensity, Science Translational Medicine, Vol: 12, Pages: eaax4144-eaax4144, ISSN: 1946-6234
Intervention planning for dengue requires reliable estimates of dengue transmission intensity. However, currentmaps of dengue risk provide estimates of disease burden or the boundaries of endemicity rather than transmissionintensity. We therefore developed a global high-resolution map of dengue transmission intensity by fittingenvironmentally driven geospatial models to geolocated force of infection estimates derived from cross-sectionalserological surveys and routine case surveillance data. We assessed the impact of interventions on dengue transmission and disease using Wolbachia-infected mosquitoes and the Sanofi-Pasteur vaccine as specific examples.We predicted high transmission intensity in all continents straddling the tropics, with hot spots in South America(Colombia, Venezuela, and Brazil), Africa (western and central African countries), and Southeast Asia (Thailand,Indonesia, and the Philippines). We estimated that 105 [95% confidence interval (CI), 95 to 114] million dengueinfections occur each year with 51 (95% CI, 32 to 66) million febrile disease cases. Our analysis suggests thattransmission-blocking interventions such as Wolbachia, even at intermediate efficacy (50% transmission reduction), might reduce global annual disease incidence by up to 90%. The Sanofi-Pasteur vaccine, targeting onlyseropositive recipients, might reduce global annual disease incidence by 20 to 30%, with the greatest impact inhigh-transmission settings. The transmission intensity map presented here, and made available for download,may help further assessment of the impact of dengue control interventions and prioritization of global publichealth efforts.
Luo XS, Imai N, Dorigatti I, 2020, Quantifying the risk of Zika virus spread in Asia during the 2015-16 epidemic in Latin America and the Caribbean: A modeling study, Travel Medicine and Infectious Disease, Vol: 33, Pages: 1-9, ISSN: 1477-8939
BackgroundNo large-scale Zika epidemic has been observed to date in Southeast Asia following the 2015-16 Latin American and the Caribbean epidemic. One hypothesis is Southeast Asian populations’ partial immunity to Zika.MethodWe estimated the two conditions for a Zika outbreak emergence in Southeast Asia: (i) the risk of Zika introduction from Latin America and the Caribbean and, (ii) the risk of autochthonous transmission under varying assumptions on population immunity. We also validated the model used to estimate the risk of introduction by comparing the estimated number of Zika seeds introduced into the United States with case counts reported by the Centers for Disease Control and Prevention (CDC).ResultsThere was good agreement between our estimates and case counts reported by the CDC. We thus applied the model to Southeast Asia and estimated that, on average, 1–10 seeds were introduced into Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam. We also found increasing population immunity levels from 0 to 90% reduced probability of autochthonous transmission by 40% and increasing individual variation in transmission further reduced the outbreak probability.ConclusionsPopulation immunity, combined with heterogeneity in transmission, can explain why no large-scale outbreak was observed in Southeast Asia during the 2015-16 epidemic.
Imai N, Cori A, Dorigatti I, et al., 2020, Report 3: Transmissibility of 2019-nCoV
Self-sustaining human-to-human transmission of the novel coronavirus (2019-nCov) is the only plausible explanation of the scale of the outbreak in Wuhan. We estimate that, on average, each case infected 2.6 (uncertainty range: 1.5-3.5) other people up to 18th January 2020, based on an analysis combining our past estimates of the size of the outbreak in Wuhan with computational modelling of potential epidemic trajectories. This implies that control measures need to block well over 60% of transmission to be effective in controlling the outbreak. It is likely, based on the experience of SARS and MERS-CoV, that the number of secondary cases caused by a case of 2019-nCoV is highly variable – with many cases causing no secondary infections, and a few causing many. Whether transmission is continuing at the same rate currently depends on the effectiveness of current control measures implemented in China and the extent to which the populations of affected areas have adopted risk-reducing behaviours. In the absence of antiviral drugs or vaccines, control relies upon the prompt detection and isolation of symptomatic cases. It is unclear at the current time whether this outbreak can be contained within China; uncertainties include the severity spectrum of the disease caused by this virus and whether cases with relatively mild symptoms are able to transmit the virus efficiently. Identification and testing of potential cases need to be as extensive as is permitted by healthcare and diagnostic testing capacity – including the identification, testing and isolation of suspected cases with only mild to moderate disease (e.g. influenza-like illness), when logistically feasible.
Imai N, Dorigatti I, Cori A, et al., 2020, Report 2: Estimating the potential total number of novel Coronavirus cases in Wuhan City, China
We estimate that a total of 4,000 cases of 2019-nCoV in Wuhan City (uncertainty range: 1,000 – 9,700) had onset of symptoms by 18th January 2020 (the last reported onset date of any case) [15].Our estimates should not be interpreted as implying the outbreak has doubled in size in the period 12th January to 18th January – delays in confirming and reporting exported cases and incomplete information about dates of symptom onset together with the still very small numbers of exported cases mean we are unable to estimate the epidemic growth rate at the current time.This estimate is based on the following assumptions:• Wuhan International Airport has a catchment population of 19 million individuals [1].• There is a mean 10-day delay between infection and detection, comprising a 5-6 day incubation period [16,17] and a 4-5 day delay from symptom onset to detection/hospitalisation of a case (the cases detected in Thailand and Japan were hospitalised 3 and 7 days after onset, respectively) [4,18].• Total volume of international travel from Wuhan over the last two months has been 3,301 passengers per day. This estimate is derived from the 3,418 foreign passengers per day in the top 20 country destinations based on 2018 IATA data [19], and uses 2016 IATA data held by Imperial College London to correct for the travel surge at Chinese New Year present in the latter data (which has not happened yet this year) and for travel to countries outside the top 20 destination list.• Exit screening (which reportedly came into force on the 15th January [13]) had no impact on exported cases reported up to 16th January. Exit screening may have reduced exports in recent days, in which case our baseline prediction may be an underestimate of the true number of cases in Wuhan.• We assume all cases in travellers flying to destinations outside mainland China are being detected at those destinations. This may well not be the case. If cases are being missed in other
Imai N, Dorigatti I, Cori A, et al., 2020, Report 1: Estimating the potential total number of novel Coronavirus cases in Wuhan City, China
We estimate that a total of 1,723 cases of 2019-nCoV in Wuhan City (95% CI: 427 – 4,471) had onset of symptoms by 12th January 2020 (the last reported onset date of any case).This estimate is based on the following assumptions:• Wuhan International Airport has a catchment population of 19 million individuals [1].• There is a mean 10-day delay between infection and detection, comprising a 5-6 day incubation period [8,9] and a 4-5 day delay from symptom onset to detection/hospitalisation of a case (the cases detected in Thailand and Japan were hospitalised 3 and 7 days after onset, respectively) [4,10].• Total volume of international travel from Wuhan over the last two months has been 3,301 passengers per day. This estimate is derived from the 3,418 foreign passengers per day in the top 20 country destinations based on 2018 IATA data [11], and uses 2016 IATA data held by Imperial College to correct for the travel surge at Chinese New Year present in the latter data (which has not happened yet this year) and for travel to countries outside the top 20 destination list.
Bhatia S, Imai N, Cuomo-Dannenburg G, et al., 2020, Estimating the number of undetected COVID-19 cases among travellers from mainland China., Wellcome open research, Vol: 5, Pages: 143-143, ISSN: 2398-502X
Background: As of August 2021, every region of the world has been affected by the COVID-19 pandemic, with more than 196,000,000 cases worldwide.Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries.Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that up to 70% (95% CI: 54% - 80%) of imported cases could remain undetected relative to the sensitivity of surveillance in Singapore. The percentage of undetected imported cases rises to 75% (95% CI 66% - 82%) when comparing to the surveillance sensitivity in multiple countries.Conclusions: Our analysis shows that a large number of COVID-19 cases remain undetected across the world. These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.
Jeffrey B, Walters CE, Ainslie KEC, et al., 2020, Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK., Wellcome open research, Vol: 5, ISSN: 2398-502X
<b>Background:</b> Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level data on mobile phone usage can be used as a proxy for actual population mobility patterns and provide a way of quantifying the impact of social distancing measures on changes in mobility. <b>Methods:</b> Here, we use two mobile phone-based datasets (anonymised and aggregated crowd level data from O2 and from the Facebook app on mobile phones) to assess changes in average mobility, both overall and broken down into high and low population density areas, and changes in the distribution of journey lengths. <b>Results:</b> We show that there was a substantial overall reduction in mobility, with the most rapid decline on the 24th March 2020, the day after the Prime Minister's announcement of an enforced lockdown. The reduction in mobility was highly synchronized across the UK. Although mobility has remained low since 26th March 2020, we detect a gradual increase since that time. We also show that the two different datasets produce similar trends, albeit with some location-specific differences. We see slightly larger reductions in average mobility in high-density areas than in low-density areas, with greater variation in mobility in the high-density areas: some high-density areas eliminated almost all mobility. <b>Conclusions:</b> These analyses form a baseline from which to observe changes in behaviour in the UK as social distancing is eased and inform policy towards the future control of SARS-CoV-2 in the UK.
Jeffrey B, Walters CE, Ainslie KEC, et al., 2020, Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK., Wellcome Open Res, Vol: 5, ISSN: 2398-502X
Background: Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level data on mobile phone usage can be used as a proxy for actual population mobility patterns and provide a way of quantifying the impact of social distancing measures on changes in mobility. Methods: Here, we use two mobile phone-based datasets (anonymised and aggregated crowd level data from O2 and from the Facebook app on mobile phones) to assess changes in average mobility, both overall and broken down into high and low population density areas, and changes in the distribution of journey lengths. Results: We show that there was a substantial overall reduction in mobility, with the most rapid decline on the 24th March 2020, the day after the Prime Minister's announcement of an enforced lockdown. The reduction in mobility was highly synchronized across the UK. Although mobility has remained low since 26th March 2020, we detect a gradual increase since that time. We also show that the two different datasets produce similar trends, albeit with some location-specific differences. We see slightly larger reductions in average mobility in high-density areas than in low-density areas, with greater variation in mobility in the high-density areas: some high-density areas eliminated almost all mobility. Conclusions: These analyses form a baseline from which to observe changes in behaviour in the UK as social distancing is eased and inform policy towards the future control of SARS-CoV-2 in the UK.
The Ebola Outbreak Epidemiology Team, Bhatia S, Cori A, et al., 2018, Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April–May, 2018: an epidemiological study, The Lancet, Vol: 392, Pages: 213-221, ISSN: 0140-6736
BackgroundOn May 8, 2018, the Government of the Democratic Republic of the Congo reported an outbreak of Ebola virus disease in Équateur Province in the northwest of the country. The remoteness of most affected communities and the involvement of an urban centre connected to the capital city and neighbouring countries makes this outbreak the most complex and high risk ever experienced by the Democratic Republic of the Congo. We provide early epidemiological information arising from the ongoing investigation of this outbreak.MethodsWe classified cases as suspected, probable, or confirmed using national case definitions of the Democratic Republic of the Congo Ministère de la Santé Publique. We investigated all cases to obtain demographic characteristics, determine possible exposures, describe signs and symptoms, and identify contacts to be followed up for 21 days. We also estimated the reproduction number and projected number of cases for the 4-week period from May 25, to June 21, 2018.FindingsAs of May 30, 2018, 50 cases (37 confirmed, 13 probable) of Zaire ebolavirus were reported in the Democratic Republic of the Congo. 21 (42%) were reported in Bikoro, 25 (50%) in Iboko, and four (8%) in Wangata health zones. Wangata is part of Mbandaka, the urban capital of Équateur Province, which is connected to major national and international transport routes. By May 30, 2018, 25 deaths from Ebola virus disease had been reported, with a case fatality ratio of 56% (95% CI 39–72) after adjustment for censoring. This case fatality ratio is consistent with estimates for the 2014–16 west African Ebola virus disease epidemic (p=0·427). The median age of people with confirmed or probable infection was 40 years (range 8–80) and 30 (60%) were male. The most commonly reported signs and symptoms in people with confirmed or probable Ebola virus disease were fever (40 [95%] of 42 cases), intense general fatigue (37 [90%] of 41 cases), an
Imai N, Ferguson NM, 2018, Targeting vaccinations for the licensed dengue vaccine: considerations for serosurvey design, PLoS ONE, Vol: 13, Pages: 1-15, ISSN: 1932-6203
BackgroundThe CYD-TDV vaccine was unusual in that the recommended target population for vaccination was originally defined not only by age, but also by transmission setting as defined by seroprevalence. WHO originally recommended countries consider vaccination against dengue with CYD-TDV vaccine in geographic settings only where prior infection with any dengue serotype, as measured by seroprevalence, was >170% in the target age group. Vaccine was not recommended in settings where seroprevalence was <50%. Test-and-vaccinate strategies suggested following new analysis by Sanofi will still require age-stratified seroprevalence surveys to optimise age-group targeting. Here we address considerations for serosurvey design in the context of vaccination program planning.MethodsTo explore how the design of seroprevalence surveys affects estimates of transmission intensity, 100 age-specific seroprevalence surveys were simulated using a beta-binomial distribution and a simple catalytic model for different combinations of age-range, survey size, transmission setting, and test sensitivity/specificity. We then used a Metropolis-Hastings Markov Chain Monte-Carlo algorithm to estimate the force of infection from each simulated dataset.ResultsSampling from a wide age-range led to more accurate estimates than merely increasing sample size in a narrow age-range. This finding was consistent across all transmission settings. The optimum test sensitivity and specificity given an imperfect test differed by setting with high sensitivity being important in high transmission settings and high specificity important in low transmission settings.ConclusionsWhen assessing vaccination suitability by seroprevalence surveys, countries should ensure an appropriate age-range is sampled, considering epidemiological evidence about the local burden of disease.
Cremin Í, Watson O, Heffernan A, et al., 2018, An infectious way to teach students about outbreaks, Epidemics, Vol: 23, Pages: 42-48, ISSN: 1755-4365
The study of infectious disease outbreaks is required to train today’s epidemiologists. A typical way to introduce and explain key epidemiological concepts is through the analysis of a historical outbreak. There are, however, few training options that explicitly utilise real-time simulated stochastic outbreaks where the participants themselves comprise the dataset they subsequently analyse. In this paper, we present a teaching exercise in which an infectious disease outbreak is simulated over a five-day period and subsequently analysed. We iteratively developed the teaching exercise to offer additional insight into analysing an outbreak. An R package for visualisation, analysis and simulation of the outbreak data was developed to accompany the practical to reinforce learning outcomes. Computer simulations of the outbreak revealed deviations from observed dynamics, highlighting how simplifying assumptions conventionally made in mathematical models often differ from reality. Here we provide a pedagogical tool for others to use and adapt in their own settings.
O'Driscoll M, Imai N, Ferguson N, et al., 2018, Spatiotemporal variability in dengue transmission intensity in Jakarta, Indonesia., Publisher: American Society of Tropical Medicine and Hygiene
Imai N, Jeffrey B, Cucunuba Z, et al., 2018, SPATIOTEMPORAL HETEROGENEITY OF DENGUE TRANSMISSION INTENSITY IN COLOMBIA, 67th Annual Meeting of the American-Society-of-Tropical-Medicine-and-Hygiene (ASTHM), Publisher: AMER SOC TROP MED & HYGIENE, Pages: 287-287, ISSN: 0002-9637
Imai N, Ferguson NM, 2017, Targeting Vaccinations for the Licensed Dengue Vaccine: Considerations for Serosurvey Design
<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>WHO recommends countries consider dengue vaccination in geographic settings only where epidemiological data indicate a high burden of disease. In defining target populations, WHO recommend that prior infection with any dengue serotype should be >70% seroprevalence. Here we address considerations for serosurvey design in the context of the newly licensed CYD-TDV vaccine.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>To explore how the design of seroprevalence surveys (age range, survey size) would affect estimates of the force of infection, for every combination of age range, total survey size, transmission setting, and test sensitivity/specificity, 100 age-specific seroprevalence surveys were simulated using a beta-binomial distribution and a simple catalytic model. The transmission intensity was then re-estimated using a Metropolis-Hastings Markov Chain Monte-Carlo algorithm.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>Sampling from a wide age range led to more accurate estimates than having a larger sample size. This finding was consistent across all transmission settings. The optimal age range to sample from differed by transmission intensity, with younger and older ages being important in high and low transmission settings respectively. The optimum test sensitivity and specificity given an imperfect test also differed by transmission setting with high sensitivity being important in high transmission settings and high specificity important in low transmission settings.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>When assessing the suitability for vaccination by seroprevalence surveys, countries should ensure that an appropriate age range is sampled, takin
Dorigatti I, Hamlet A, Aguas R, et al., 2017, International risk of yellow fever spread from the ongoing outbreak in Brazil, December 2016 to May 2017, EUROSURVEILLANCE, Vol: 22, Pages: 1-4, ISSN: 1560-7917
States in south-eastern Brazil were recently affected by the largest Yellow Fever (YF) outbreak seen in a decade in Latin America. Here we provide a quantitative assessment of the risk of travel-related international spread of YF indicating that the United States, Argentina, Uruguay, Spain, Italy and Germany may have received at least one travel-related YF case capable of seeding local transmission. Mitigating the risk of imported YF cases seeding local transmission requires heightened surveillance globally.
Dorigatti I, Hamlet A, Aguas R, et al., 2017, International risk of yellow fever spread from the ongoing outbreak in Brazil, Publisher: bioRxiv
The largest Yellow Fever (YF) outbreak in a decade in Latin America is underway in the Southeast of Brazil. In this article we provide a quantitative assessment of the risk of travel-related international spread of YF. We argue that mitigating the risk of imported YF cases seeding local transmission requires heightened surveillance in the southern United States, Latin America (especially Argentina, Chile and Uruguay) and Europe (especially Portugal, Spain, Italy and Germany).
Imai N, Rodriguez-Barraquer I, Hinsley W, et al., 2017, MAPPING THE GLOBAL ESTIMATES OF DENGUE SEROPREVALENCE AND TRANSMISSION INTENSITY, 66th Annual Meeting of the American-Society-of-Tropical-Medicine-and-Hygiene (ASTMH), Publisher: AMER SOC TROP MED & HYGIENE, Pages: 193-193, ISSN: 0002-9637
Cattarino L, Rodriguez-Barraquer I, Cummings D, et al., 2017, MAPPING GLOBAL VARIATION IN DENGUE TRANSMISSION INTENSITY AND ASSESSING THE IMPACT OF CONTROL STRATEGIES, 66th Annual Meeting of the American-Society-of-Tropical-Medicine-and-Hygiene (ASTMH), Publisher: AMER SOC TROP MED & HYGIENE, Pages: 193-193, ISSN: 0002-9637
Imai N, Dorigatti I, Cauchemez S, et al., 2016, Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries, PLOS Neglected Tropical Diseases, Vol: 10, ISSN: 1935-2735
BackgroundDespite being the most widely distributed mosquito-borne viral infection, estimates of dengue transmission intensity and associated burden remain ambiguous. With advances in the development of novel control measures, obtaining robust estimates of average dengue transmission intensity is key for assessing the burden of disease and the likely impact of interventions.Methodology/Principle FindingsWe estimated the force of infection (λ) and corresponding basic reproduction numbers (R0) by fitting catalytic models to age-stratified incidence data identified from the literature. We compared estimates derived from incidence and seroprevalence data and assessed the level of under-reporting of dengue disease. In addition, we estimated the relative contribution of primary to quaternary infections to the observed burden of dengue disease incidence. The majority of R0 estimates ranged from one to five and the force of infection estimates from incidence data were consistent with those previously estimated from seroprevalence data. The baseline reporting rate (or the probability of detecting a secondary infection) was generally low (<25%) and varied within and between countries.Conclusions/SignificanceAs expected, estimates varied widely across and within countries, highlighting the spatio-temporally heterogeneous nature of dengue transmission. Although seroprevalence data provide the maximum information, the incidence models presented in this paper provide a method for estimating dengue transmission intensity from age-stratified incidence data, which will be an important consideration in areas where seroprevalence data are not available.
Imai N, Dorigatti I, Cauchemez S, et al., 2015, ESTIMATING DENGUE TRANSMISSION INTENSITY FROM INCIDENCE DATA IN MULTIPLE COUNTRIES, Publisher: AMER SOC TROP MED & HYGIENE, Pages: 234-234, ISSN: 0002-9637
Ferguson NM, Imai N, Nedjati-Gilani G, et al., 2015, ESTABLISHING THE WMEL STRAIN OF WOLBACHIA IN <i>AEDES AEGYPTI</i> POPULATIONS PREDICTED TO REDUCE THE DISEASE BURDEN FROM DENGUE BY AT LEAST TWO-THIRDS, Publisher: AMER SOC TROP MED & HYGIENE, Pages: 179-179, ISSN: 0002-9637
Imai N, Dorigatti I, Cauchemez S, et al., 2015, Estimating Dengue Transmission Intensity from Sero-Prevalence Surveys in Multiple Countries, PLOS Neglected Tropical Diseases, Vol: 9, ISSN: 1935-2735
BackgroundEstimates of dengue transmission intensity remain ambiguous. Since the majority of infectionsare asymptomatic, surveillance systems substantially underestimate true rates of infection.With advances in the development of novel control measures, obtaining robustestimates of average dengue transmission intensity is key for assessing both the burden ofdisease from dengue and the likely impact of interventions.Methodology/Principal FindingsThe force of infection (λ) and corresponding basic reproduction numbers (R0) for denguewere estimated from non-serotype (IgG) and serotype-specific (PRNT) age-stratified seroprevalencesurveys identified from the literature. The majority of R0 estimates ranged from1–4. Assuming that two heterologous infections result in complete immunity produced up totwo-fold higher estimates of R0 than when tertiary and quaternary infections were included.λ estimated from IgG data were comparable to the sum of serotype-specific forces of infectionderived from PRNT data, particularly when inter-serotype interactions were allowed for.Conclusions/SignificanceOur analysis highlights the highly heterogeneous nature of dengue transmission. How underlyingassumptions about serotype interactions and immunity affect the relationship betweenthe force of infection and R0 will have implications for control planning. While PRNTdata provides the maximum information, our study shows that even the much cheaperELISA-based assays would provide comparable baseline estimates of overall transmissionintensity which will be an important consideration in resource-constrained settings.
Imai N, White MT, Ghani AC, et al., 2014, Transmission and Control of Plasmodium knowlesi: A Mathematical Modelling Study, PLOS Neglected Tropical Diseases, Vol: 8, ISSN: 1935-2735
Introduction: Plasmodium knowlesi is now recognised as a leading cause of malaria in Malaysia. As humans come intoincreasing contact with the reservoir host (long-tailed macaques) as a consequence of deforestation, assessing the potentialfor a shift from zoonotic to sustained P. knowlesi transmission between humans is critical.Methods: A multi-host, multi-site transmission model was developed, taking into account the three areas (forest, farm, andvillage) where transmission is thought to occur. Latin hypercube sampling of model parameters was used to identifyparameter sets consistent with possible prevalence in macaques and humans inferred from observed data. We then explorethe consequences of increasing human-macaque contact in the farm, the likely impact of rapid treatment, and the use oflong-lasting insecticide-treated nets (LLINs) in preventing wider spread of this emerging infection.Results: Identified model parameters were consistent with transmission being sustained by the macaques with spill over infectionsinto the human population and with high overall basic reproduction numbers (up to 2267). The extent to which macaques foragein the farms had a non-linear relationship with human infection prevalence, the highest prevalence occurring when macaquesforage in the farms but return frequently to the forest where they experience higher contact with vectors and hence sustaintransmission. Only one of 1,046 parameter sets was consistent with sustained human-to-human transmission in the absence ofmacaques, although with a low human reproduction number (R0H = 1.04). Simulations showed LLINs and rapid treatment providepersonal protection to humans with maximal estimated reductions in human prevalence of 42% and 95%, respectively.Conclusion: This model simulates conditions where P. knowlesi transmission may occur and the potential impact of controlmeasures. Predictions suggest that conventional control measures are sufficient at reducing the risk of infection in humans
Harvala H, McIntyre CL, Imai N, et al., 2012, High Seroprevalence of Enterovirus Infections in Apes and Old World Monkeys, Emerging Infectious Diseases, Vol: 18, Pages: 283-286, ISSN: 1080-6059
To estimate population exposure of apes and Old World monkeys in Africa to enteroviruses (EVs), we conducted a seroepidemiologic study of serotype-specific neutralizing antibodies against 3 EV types. Detection of species A, B, and D EVs infecting wild chimpanzees demonstrates their potential widespread circulation in primates.
Imai N, Rujeni N, Nausch N, et al., 2011, Exposure, infection, systemic cytokine levels and antibody responses in young children concurrently exposed to schistosomiasis and malaria, Parasitology, Vol: 138, Pages: 1519-1533, ISSN: 1469-8161
Despite the overlapping distribution of Schistosoma haematobium and Plasmodium falciparum infections, few studies have investigated early immune responses to both parasites in young children resident in areas co-endemic for the parasites. This study measures infection levels of both parasites and relates them to exposure and immune responses in young children. Levels of IgM, IgE, IgG4 directed against schistosome cercariae, egg and adult worm and IgM, IgG directed against P. falciparum schizonts and the merozoite surface proteins 1 and 2 together with the cytokines IFN-γ, IL-4, IL-5, IL-10 and TNF-α were measured by ELISA in 95 Zimbabwean children aged 1-5 years. Schistosome infection prevalence was 14·7% and that of Plasmodium infection was 0% in the children. 43. 4% of the children showed immunological evidence of exposure to schistosome parasites and 13% showed immunological evidence of exposure to Plasmodium parasites. Schistosome-specific responses, indicative of exposure to parasite antigens, were positively associated with cercariae-specific IgE responses, while Plasmodium-specific responses, indicative of exposure to parasite antigens, were negatively associated with responses associated with protective immunity against Plasmodium. There was no significant association between schistosome-specific and Plasmodium-specific responses. Systemic cytokine levels rose with age as well as with schistosome infection and exposure. Overall the results show that (1) significantly more children are exposed to schistosome and Plasmodium infection than those currently infected and; (2) the development of protective acquired immunity commences in early childhood, although its effects on infection levels and pathology may take many years to become apparent.
Mutapi F, Imai N, Nausch N, et al., 2011, Schistosome infection intensity is inversely related to auto-reactive antibody levels., PLOS One, Vol: 6, ISSN: 1932-6203
In animal experimental models, parasitic helminth infections can protect the host from auto-immune diseases. We conducted a population-scale human study investigating the relationship between helminth parasitism and auto-reactive antibodies and the subsequent effect of anti-helminthic treatment on this relationship. Levels of antinuclear antibodies (ANA) and plasma IL-10 were measured by enzyme linked immunosorbent assay in 613 Zimbabweans (aged 2-86 years) naturally exposed to the blood fluke Schistosoma haematobium. ANA levels were related to schistosome infection intensity and systemic IL-10 levels. All participants were offered treatment with the anti-helminthic drug praziquantel and 102 treated schoolchildren (5-16 years) were followed up 6 months post-antihelminthic treatment. ANA levels were inversely associated with current infection intensity but were independent of host age, sex and HIV status. Furthermore, after allowing for the confounding effects of schistosome infection intensity, ANA levels were inversely associated with systemic levels of IL-10. ANA levels increased significantly 6 months after anti-helminthic treatment. Our study shows that ANA levels are attenuated in helminth-infected humans and that anti-helminthic treatment of helminth-infected people can significantly increase ANA levels. The implications of these findings are relevant for understanding both the aetiology of immune disorders mediated by auto-reactive antibodies and in predicting the long-term consequences of large-scale schistosomiasis control programs.
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