ORCID Profile
0000-0002-1811-1310
Current Organisation
James Cook University
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Publisher: Cold Spring Harbor Laboratory
Date: 19-07-2021
DOI: 10.1101/2021.07.16.21260642
Abstract: The Australian National Cabinet four-step plan to transition to post-pandemic re-opening begins with vaccination to achieve herd protection and protection of the health system against a surge in COVID-19 cases. Assuming a pre-vaccination reproduction number for the Delta variant of 5, we show that for the current Mixed program of vaccinating over 60s with AstraZeneca and 16-60s with Pfizer we would not achieve herd immunity. We would need to cover 85% of the population (including many 5-16 year-olds to achieve herd immunity). At lower reproduction number of 3 and our current Mixed strategy, we can achieve herd immunity without vaccinating 5-15 year olds. This will be achieved at a 60% coverage pursuing a strategy targetting high transmitters or 70% coverage using a strategy targetting the vulnerable first. A reproduction number of 7 precludes achieving herd immunity, however vaccination is able to prevent 75% of deaths compared with no vaccination. We also examine the impact of vaccination on death in the event that herd immunity is not achieved. Direct effects of vaccination on reducing death are very good for both Pfizer and AstraZeneca vaccines. However we estimate that the Mixed or Pfizer program performs better than the AstraZeneca program. Furthermore, vaccination levels below the herd immunity threshold can lead to substantial (albeit incomplete) indirect protection for both vaccinated and unvaccinated populations. Given the potential for not reaching herd immunity, we need to consider what level of severe disease and death is acceptable, balanced against the consequences of ongoing aggressive control strategies.
Publisher: Elsevier BV
Date: 2019
Publisher: Cold Spring Harbor Laboratory
Date: 19-05-2020
DOI: 10.1101/2020.05.12.20099036
Abstract: Australia is one of a few countries which has managed to control COVID-19 epidemic before a major epidemic took place. Currently with just under 7000 cases and 100 deaths, Australia is seeing less than 20 new cases per day. This is a positive outcome, but makes estimation of current effective reproduction numbers difficult to estimate. Australia, like much of the world is poised to step out of lockdown and looking at which measures to relax first. We use age-based contact matrices, calibrated to Chinese data on reproduction numbers and difference in infectiousness and susceptibility of children to generate next generation matrices (NGMs) for Australia. These matrices have a spectral radius of 2.49, which is hence our estimated basic reproduction number for Australia. The effective reproduction number (Reff) for Australia during the April/May lockdown period is estimated by other means to be around 0.8. We simulate the impact of lockdown on the NGM by first applying observations through Google Mobility Report for Australia at 3 locations: home (increased contacts by 18%), work (reduced contacts by 34%) and other (reduced contacts by 40%), and we reduce schools to 3% reflecting attendance rates during lockdown. Applying macro-distancing to the NGM leads to a spectral radius of 1.76. We estimate that the further reduction of the reproduction number to current levels of Reff = 0.8 is achieved by a micro-distancing factor of 0.26. That is, in a given location, people are 26% as likely as usual to have an effective contact with another person. We apply both macro and micro-distancing to the NGMs to examine the impact of different exit strategies. We find that reopening schools is estimated to reduce Reff from 0.8 to 0.78. This is because increase in school contact is offset by decrease in home contact. The NGMs all estimate that adults aged 30-50 are responsible for the majority of transmission. We also find that micro-distancing is critically important to maintain Reff . There is considerable uncertainty in these estimates and a sensitivity and uncertainty analysis is presented.
Publisher: Wiley
Date: 11-10-2021
DOI: 10.5694/MJA2.51263
Publisher: MDPI AG
Date: 28-04-2020
Abstract: On 31 December 2019, the World Health Organization (WHO) was notified of a novel coronavirus disease in China that was later named COVID-19. On 11 March 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on 27 February 2020. This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria. We estimated the early transmissibility via time-varying reproduction number based on the Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected in idual and the infector), and adjusted for disease importation. By 11 April 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% confidence interval (CI): 0.05–0.10) with a doubling time of 9.84 days (95% CI: 7.28–15.18). Separately for imported cases (travel-related) and local cases, the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using a three-weekly window while adjusting for imported cases. The estimated reproduction number was 4.98 (95% CrI: 2.65–8.41) at day 22 (19 March 2020), peaking at 5.61 (95% credible interval (CrI): 3.83–7.88) at day 25 (22 March 2020). The median reproduction number over the study period was 2.71 and the latest value on 11 April 2020, was 1.42 (95% CrI: 1.26–1.58). These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.
Publisher: Wiley
Date: 27-06-2020
DOI: 10.1002/EDN3.114
Publisher: Public Library of Science (PLoS)
Date: 17-03-2015
Publisher: No publisher found
Date: 2016
Publisher: MDPI AG
Date: 08-01-2021
Abstract: Arthropod-borne viruses (Arboviruses) continue to generate significant health and economic burdens for people living in endemic regions. Of these viruses, some of the most important (e.g., dengue, Zika, chikungunya, and yellow fever virus), are transmitted mainly by Aedes mosquitoes. Over the years, viral infection control has targeted vector population reduction and inhibition of arboviral replication and transmission. This control includes the vector control methods which are classified into chemical, environmental, and biological methods. Some of these control methods may be largely experimental (both field and laboratory investigations) or widely practised. Perceptively, one of the biological methods of vector control, in particular, Wolbachia-based control, shows a promising control strategy for eradicating Aedes-borne arboviruses. This can either be through the artificial introduction of Wolbachia, a naturally present bacterium that impedes viral growth in mosquitoes into heterologous Aedes aegypti mosquito vectors (vectors that are not natural hosts of Wolbachia) thereby limiting arboviral transmission or via Aedes albopictus mosquitoes, which naturally harbour Wolbachia infection. These strategies are potentially undermined by the tendency of mosquitoes to lose Wolbachia infection in unfavourable weather conditions (e.g., high temperature) and the inhibitory competitive dynamics among co-circulating Wolbachia strains. The main objective of this review was to critically appraise published articles on vector control strategies and specifically highlight the use of Wolbachia-based control to suppress vector population growth or disrupt viral transmission. We retrieved studies on the control strategies for arboviral transmissions via arthropod vectors and discussed the use of Wolbachia control strategies for eradicating arboviral diseases to identify literature gaps that will be instrumental in developing models to estimate the impact of these control strategies and, in essence, the use of different Wolbachia strains and features.
Publisher: Massachusetts Medical Society
Date: 21-07-2016
DOI: 10.1056/NEJMC1604709
Publisher: Cold Spring Harbor Laboratory
Date: 27-03-2020
DOI: 10.1101/2020.03.22.20041244
Abstract: Following the outbreak of novel Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) or COVID-19 in Wuhan, China late 2019, different countries have put in place interventions such as travel ban, proper hygiene, and social distancing to slow the spread of this novel virus. We evaluated the effects of travel bans in the Australia context and projected the epidemic until May 2020. Our modelling results closely align with observed cases in Australia indicating the need for maintaining or improving on the control measures to slow down the virus.
Publisher: Public Library of Science (PLoS)
Date: 23-07-2020
Publisher: MDPI AG
Date: 17-04-2019
Abstract: In February 2019, a major flooding event occurred in Townsville, North Queensland, Australia. Here we present a prediction of the occurrence of mosquito-borne diseases (MBDs) after the flooding. We used a mathematical modelling approach based on mosquito population abundance, survival, and size as well as current infectiousness to predict the changes in the occurrences of MBDs due to flooding in the study area. Based on 2019 year-to-date number of notifiable MBDs, we predicted an increase in number of cases, with a peak at 104 by one-half month after the flood receded. The findings in this study indicate that Townsville may see an upsurge in the cases of MBDs in the coming days. However, the burden of diseases will go down again if the mosquito control program being implemented by the City Council continues. As our predictions focus on the near future, longer term effects of flooding on the occurrence of mosquito-borne diseases need to be studied further.
Publisher: Cambridge University Press (CUP)
Date: 2020
DOI: 10.1017/S0950268820001740
Abstract: Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number ( R ( t )) and basic reproduction number ( R 0 ) – this also enables us to estimate the initial daily transmission rate ( β 0 ). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R ( t ) is strictly greater than one from 13 April till 7 May 2020. The R 0 is estimated to be 2.42 with credible interval: (2.37–2.47). Comparing this with the R ( t ) shows that control measures are working but not effective enough to keep R ( t ) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.
Publisher: Elsevier BV
Date: 05-2014
DOI: 10.1016/J.BIOSYSTEMS.2014.03.006
Abstract: Tuberculosis is a bacterial disease caused by Mycobacterium tuberculosis (TB). The risk for TB infection greatly increases with HIV infection TB disease occurs in 7-10% of patients with HIV infection each year, increasing the potential for transmission of drug-resistant Mycobacterium tuberculosis strains. In this paper a deterministic model is presented and studied for the transmission of TB-HIV/AIDS co-infection. Optimal control theory is then applied to investigate optimal strategies for controlling the spread of the disease using treatment of infected in iduals with TB as the system control variables. Various combination strategies were examined so as to investigate the impact of the controls on the spread of the disease. And incremental cost-effectiveness ratio (ICER) was used to investigate the cost effectiveness of all the control strategies. Our results show that the implementation of the combination strategy involving the prevention of treatment failure in drug-sensitive TB infectious in iduals and the treatment of in iduals with drug-resistant TB is the most cost-effective control strategy. Similar results were obtained with different objective functionals involving the minimization of the number of in iduals with drug-sensitive TB-only and drug-resistant TB-only with the efforts involved in applying the control.
Publisher: Cold Spring Harbor Laboratory
Date: 20-05-2020
DOI: 10.1101/2020.05.16.20104471
Abstract: Following the importation of Covid-19 into Nigeria on the 27 February 2020 and then the outbreak, the question is: how do we anticipate the progression of the ongoing epidemics following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of Covid-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R_0). This also enables us to estimate the daily transmission rate (β) that determines the effect of social distancing. We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on Covid-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 1.78. Most importantly, the R(t) is strictly greater than one from April 13 till 7 May 2020. The R_0 is estimated to be 2.42 with credible interval: (2.37, 2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below one. Also, the estimated fractional reported symptomatic cases are between 10 to 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.
No related grants have been discovered for Adeshina Adekunle.