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Oxford Centre for Tropical Medicine and Global Health
Use of population pharmacokinetic‐pharmacodynamic modelling to inform antimalarial dose optimization in infants
Infants bear a significant malaria burden but are usually excluded from participating in early dose optimization studies that inform dosing regimens of antimalarial therapy. Unlike older children, infants' exclusion from early‐phase trials has resulted in limited evidence to guide accurate dosing of antimalarial treatment for uncomplicated malaria or malaria‐preventive treatment in this vulnerable population. Subsequently, doses used in infants are often extrapolated from older children or adults, with the potential for under‐ or overdosing. Population pharmacokinetic‐pharmacodynamic (PK‐PD) modelling, a quantitative methodology that applies mathematical and statistical techniques, can aid the design of clinical studies in infants that collect sparse pharmacokinetic data as well as support the analysis of such data to derive optimized antimalarial dosing in this complex and at‐risk yet understudied subpopulation. In this review, we reflect on what PK‐PD modelling can do in programmatic settings of most malaria‐endemic areas and how it can be used to inform antimalarial dose optimization for preventive and curative treatment of uncomplicated malaria in infants. We outline key developmental physiological changes that affect drug exposure in early life, the challenges of conducting dose optimization studies in infants, and examples of how PK‐PD modelling has previously informed antimalarial dose optimization in this subgroup. Additionally, we discuss the limitations and gaps of PK‐PD modelling when used for dose optimization in infants. To utilize modelling well, there is a need to generate useful, sparse, PK and PD data in this subpopulation to inform antimalarial optimal dosing in infancy.
Impact of targeted drug administration and intermittent preventive treatment for forest goers using artesunate–pyronaridine to control malaria outbreaks in Cambodia
Introduction: The national malaria programme of Cambodia targets the rapid elimination of all human malaria by 2025. As clinical cases decline to near-elimination levels, a key strategy is the rapid identification of malaria outbreaks triggering effective action to interrupt local transmission. We report a comprehensive, multipronged management approach in response to a 2022 Plasmodium falciparum outbreak in Kravanh district, western Cambodia. Methods: The provincial health department of Pursat in conjunction with the Center for Parasitology, Entomology and Malaria Control (CNM) identified villages where transmission was occurring using clinical records, and initiated various interventions, including the distribution of insecticide-treated bed nets, running awareness campaigns, and implementing fever screening with targeted drug administration. Health stations were set up at forest entry points, and later, targeted drug administrations with artesunate–pyronaridine (Pyramax) and intermittent preventive treatment for forest goers (IPTf) were implemented in specific village foci. Data related to adherence and adverse events from IPTf and TDA were collected. The coverage rates of interventions were calculated, and local malaria infections were monitored. Results: A total of 942 individuals were screened through active fever surveillance in villages where IPTf and TDA were conducted. The study demonstrated high coverage and adherence rates in the targeted villages, with 92% (553/600) coverage in round one and 65% (387/600) in round two. Adherence rate was 99% (551/553) in round one and 98% (377/387) in round two. The study found that forest goers preferred taking Pyramax over repeated testing consistent with the coverage rates: 92% in round one compared to 65% in round two. All individuals reachable through health stations or mobile teams reported complete IPTf uptake. No severe adverse events were reported. Only six individuals reported mild adverse events, such as loss of energy, fever, abdominal pain, diarrhoea, and muscle aches. Two individuals attributed their symptoms to heavy alcohol intake following prophylaxis. Conclusions: The targeted malaria outbreak response demonstrated high acceptability, safety, and feasibility of the selected interventions. Malaria transmission was rapidly controlled using the available community resources. This experience suggests the effectiveness of the programmatic response for future outbreaks.
The mechanism of artemisinin resistance of Plasmodium falciparum malaria parasites originates in their initial transcriptional response.
Abstract The emergence and spread of artemisinin resistant Plasmodium falciparum, first in the Greater Mekong Subregion (GMS), and now in East Africa, is a major threat to global malaria eliminations ambitions. To investigate the artemisinin resistance mechanism, transcriptome analysis was conducted of 577 P. falciparum isolates collected in the GMS between 2016–2018. A specific artemisinin resistance-associated transcriptional profile was identified that involves a broad but discrete set of biological functions related to proteotoxic stress, host cytoplasm remodeling and REDOX metabolism. The artemisinin resistance-associated transcriptional profile evolved from initial transcriptional responses of susceptible parasites to artemisinin. The genetic basis for this adapted response is likely to be complex.
A Clinically Oriented antimicrobial Resistance surveillance Network (ACORN): pilot implementation in three countries in Southeast Asia, 2019-2020.
Background: Case-based surveillance of antimicrobial resistance (AMR) provides more actionable data than isolate- or sample-based surveillance. We developed A Clinically Oriented antimicrobial Resistance surveillance Network (ACORN) as a lightweight but comprehensive platform, in which we combine clinical data collection with diagnostic stewardship, microbiological data collection and visualisation of the linked clinical-microbiology dataset. Data are compatible with WHO GLASS surveillance and can be stratified by syndrome and other metadata. Summary metrics can be visualised and fed back directly for clinical decision-making and to inform local treatment guidelines and national policy. Methods: An ACORN pilot was implemented in three hospitals in Southeast Asia (1 paediatric, 2 general) to collect clinical and microbiological data from patients with community- or hospital-acquired pneumonia, sepsis, or meningitis. The implementation package included tools to capture site and laboratory capacity information, guidelines on diagnostic stewardship, and a web-based data visualisation and analysis platform. Results: Between December 2019 and October 2020, 2294 patients were enrolled with 2464 discrete infection episodes (1786 community-acquired, 518 healthcare-associated and 160 hospital-acquired). Overall, 28-day mortality was 8.7%. Third generation cephalosporin resistance was identified in 54.2% (39/72) of E. coli and 38.7% (12/31) of K. pneumoniae isolates . Almost a quarter of S. aureus isolates were methicillin resistant (23.0%, 14/61). 290/2464 episodes could be linked to a pathogen, highlighting the level of enrolment required to achieve an acceptable volume of isolate data. However, the combination with clinical metadata allowed for more nuanced interpretation and immediate feedback of results. Conclusions: ACORN was technically feasible to implement and acceptable at site level. With minor changes from lessons learned during the pilot ACORN is now being scaled up and implemented in 15 hospitals in 9 low- and middle-income countries to generate sufficient case-based data to determine incidence, outcomes, and susceptibility of target pathogens among patients with infectious syndromes.
Destruction, disruption and disaster: Sudan’s health system amidst armed conflict
AbstractThe ongoing armed conflict in Sudan has resulted in a deepening humanitarian crisis with significant implications for the country's health system, threatening its collapse. This article examines the destruction, disruption, and disastrous consequences inflicted upon Sudan's health system. The conflict has led to the severe compromise of healthcare facilities, with only one-third of hospitals in conflict zones operational. Artillery attacks, forced militarization, power outages, and shortages of medical supplies and personnel have further crippled the health system. The exodus of health workers and escalating violence have exacerbated the crisis. Disrupted service delivery has resulted in the interruption of essential health services, including obstetric care, emergency services, and dialysis. Financial losses to the health system are estimated at $700 million, impacting an already underfunded sector. We identify that in addition to restoration of peace and mobilization of urgent aid, immediate prioritization of the reconstruction of the health system is crucial to mitigate the long-term consequences of the war. Rebuilding a resilient health system is sine qua non for Sudan's progress towards universal health.
Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach.
BackgroundMalaria continues to pose a significant health threat. Rapid identification of malaria infections and the deployment of active surveillance tools are crucial for achieving malaria elimination in regions where malaria is endemic, such as certain areas of Thailand. In this study, an anomaly detection system is introduced as an early warning mechanism for potential malaria outbreaks in countries like Thailand.MethodsUnsupervised clustering-based, and time series-based anomaly detection algorithms are developed and compared to identify abnormal malaria activity in Thailand. Additionally, a user interface tailored for anomaly detection is designed, enabling the Thai malaria surveillance team to utilize these algorithms and visualize regions exhibiting unusual malaria patterns.ResultsNine distinct anomaly detection algorithms we developed. Their efficacy in pinpointing verified outbreaks was assessed using malaria case data from Thailand spanning 2012 to 2022. The historical average threshold-based anomaly detection method triggered three times fewer alerts, while correctly identifying the same number of verified outbreaks when compared to the current method used in Thailand. A limitation of this analysis is the small number of verified outbreaks; further consultation with the Division of Vector Borne Disease could help identify more verified outbreaks. The developed dashboard, designed specifically for anomaly detection, allows disease surveillance professionals to easily identify and visualize unusual malaria activity at a provincial level across Thailand.ConclusionAn enhanced early warning system is proposed to bolster malaria elimination efforts for countries with a similar malaria profile to Thailand. The developed anomaly detection algorithms, after thorough comparison, have been optimized for integration with the current malaria surveillance infrastructure. An anomaly detection dashboard for Thailand is built and supports early detection of abnormal malaria activity. In summary, the proposed early warning system enhances the identification process for provinces at risk of outbreaks and offers easy integration with Thailand's established malaria surveillance framework.
Analysis and optimal control measures of diseases in cassava population
AbstractCassava amongst other crops is ranked currently as the third most essential source of carbohydrate in the tropics and a major food source in Africa. In spite of these gains, cassava production is highly threatened by pathogen‐causing diseases. In this study, a deterministic compartmental model is developed to investigate the effect of these diseases on cassava production. The local and global stabilities of the disease free and endemic equilibrium states of the model without control are analyzed. The model is extended to incorporate control techniques necessary in eradicating cassava diseases. Optimal control theory is applied on the control model to explore the impact of the introduction of resistant cassava plant, the use of all other traditional practices and the application of pesticides in the prevention and eradication of diseases in cassava production. Numerical simulation of the models are carried out and results obtained indicates that introduction of control measures should be done largely at the onset of cassava planting by introducing resistant cassava stems to reduce the spread of the disease and also maintain minimum cost for production and thereby maximize profit from its yield. The cost effectiveness analysis agreed with our earlier analysis and shows that the best and effective control is the use of resistant cassava stem together with the IPM approach.
Rifampicin resistance patterns and dynamics of tuberculosis and drug-resistant tuberculosis in Enugu, South Eastern Nigeria
Introduction: Tuberculosis (TB) continues to be a public health problem globally. The burden is further exacerbated in developing countries like Nigeria, by poor diagnosis, management and treatment, as well as rapid emergence of drug-resistant TB. This study was conducted to evaluate the prevalence of drug-resistant TB, determine the rpoB gene mutation patterns of Mycobacterium tuberculosis (MTB) and model the dynamics of multidrug resistant TB (MDR-TB) in Enugu, Nigeria. Methodology: A total of 868 samples, from patients accessing DOTS services in designated centres within the zone, were screened by sputum-smear microscopy, while 207 samples were screened by Nucleic Acid Amplification (Xpert® MTB/Rif) Test (NAAT). A deterministic model was formulated to study the transmission dynamics of TB and MDR-TB, using live data generated through epidemiological study. Results: The results showed TB prevalence values of 22.1% and 21.3% by sputum-smear and NAAT assays, respectively. Analysis of the rifampicin resistance patterns showed the highest occurrence of mutations (50%) along codons 523 – 527. Factors such as combination therapy, multiple therapy and compliance to treatment had influence on both prevalence and development of TB drug resistance in the population. Conclusions: This first documentation of Rifampicin resistance patterns in MTB from Nigeria shows that a majority of rpoB gene mutations occurred along codons 523 to 527, contrary to the widely reported codon 531 mutation and that multiple interventions such as combination therapy, with good compliance to treatment are needed to reduce both prevalence and development of TB drug resistance in the population.
Dynamical System Analysis and Optimal Control Measures of Lassa Fever Disease Model
Lassa fever is an animal-borne acute viral illness caused by the Lassa virus. This disease is endemic in parts of West Africa including Benin, Ghana, Guinea, Liberia, Mali, Sierra Leone, and Nigeria. We formulate a mathematical model for Lassa fever disease transmission under the assumption of a homogeneously mixed population. We highlighted the basic factors influencing the transmission of Lassa fever and also determined and analyzed the important mathematical features of the model. We extended the model by introducing various control intervention measures, like external protection, isolation, treatment, and rodent control. The extended model was analyzed and compared with the basic model by appropriate qualitative analysis and numerical simulation approach. We invoked the optimal control theory so as to determine how to reduce the spread of the disease with minimum cost.
Analysis and Optimal Control Measures of a Typhoid Fever Mathematical Model for Two Socio-Economic Populations
Typhoid fever is an infectious disease that affects humanity worldwide; it is particularly dangerous in areas with communities of a lower socio-economic status, where many individuals are exposed to a dirty environment and unclean food. A mathematical model is formulated to analyze the impact of control measures such as vaccination of susceptible humans, treatment of infected humans and sanitation in different socio-economic communities. The model assumed that the population comprises of two socio-economic classes. The essential dynamical system analysis of our model was appropriately carried out. The impact of the control measures was analyzed, and the optimal control theory was applied on the control model to explore the impact of the different control measures. Numerical simulation of the models and the optimal controls were carried out and the obtained results indicate that the overall combination of the control measures eradicates typhoid fever in the population, but the controls are more optimal in higher socio-economic status communities.
MATHEMATICAL MODELLING APPROACH OF THE STUDY OF EBOLA VIRUS DISEASE TRANSMISSION DYNAMICS IN A DEVELOPING COUNTRY.
Background: Ebola Virus causes disease both in human and non-human primates especially in developing countries. Materials and Methods: Here we studied the spread of Ebola virus in and hence obtained a system of equations comprising of eighteen equations which completely described the transmission of Ebola Virus in a population where control measures like vaccination, treatment, quarantine, isolation of infectious patients while on treatment and use of condom were incorporated and a major source of contacting the disease which is the traditional washing of dead bodies was also incorporated. We investigated the local stability of the disease-free equilibrium using the Jacobian approach and the global stability using the center manifold theorem. We also investigated the local and global stability of the endemic theorem by constructing a Lyapunov function using the LaSalle’s Invariant principle. Results: This modeled system of equations was analyzed, and result showed that the disease-free equilibrium where both local and globally stable and that the system exhibits a forward bifurcation. The endemic equilibrium also was showed to be stable when the reproduction number is greater than one. Conclusions: Furthermore, numerical simulations were carried out to further see the impacts of the various control measures on the various compartments of the population. Our graphs show that isolation is the best option for an infectious person to be treated to avoid the disease been spread further and leads to quicker and better recovery.
Dynamical System Analysis of a Lassa Fever Model with Varying Socioeconomic Classes
Lassa fever is an animal-borne acute viral illness caused by Lassa virus. It poses a serious health challenge around the world today, especially in West African countries like Ghana, Benin, Guinea, Liberia, Mali, Sierra Leone, and Nigeria. In this work, we formulate a multiple-patch Lassa fever model, where each patch denotes a socioeconomic class (SEC). Some of the important epidemiological features such as basic reproduction number of the model were determined and analysed accordingly. We further investigated how varying SECs affect the transmission dynamics of Lassa fever. We analysed the required state at which each SEC is responsible in driving the Lassa fever disease outbreak. Sensitivity analyses were carried out to determine the importance of model parameters to the disease transmission and prevalence. We carried out numerical simulation to support our analytical results. Finally, we extend some of the results of the 2-patch model to the general n -patch model.
Approximate Solution of the Fractional Order Sterile Insect Technology Model via the Laplace–Adomian Decomposition Method for the Spread of Zika Virus Disease
Sterile insect technology (SIT) is an environmental-friendly method which depends on the release of sterile male mosquitoes that compete with the wild male mosquitoes and mate with wild female mosquitoes, which leads to the production of no offspring and as such reduces the population of Zika virus vector population over time, thereby eliminating the spread of Zika virus in a population. The fractional order sterile insect technology (SIT) model to reduce the spread of Zika virus disease is considered in this present work. We employed the use Laplace–Adomian decomposition method (LADM) to determine an analytical (approximate) solution of the model. The Laplace–Adomian decomposition method (LADM) produced a solution in form of an infinite series that further converges to the exact value. We compared solutions of the fractional model with the classical case using our plots and discovered that the fractional order has more degree of freedom and as such the system can be varied to get many preferred responses of the different classes of the model as the fraction (β) could be varied to the desired rate, say 0.7, 0.4, etc. We have been able to show that LADM can be used to solve an SIT model which has never been done before in literature.