understand interventions against malaria in humans
Inaugural dissertation zur
Erlangung der Würde eines Doktors der Philosophie vorgelegt der
Philosophisch-Naturwissenschaftlichen Fakultät der Universität Basel
von
Flavia Camponovo von Basel (BS)
Basel, 2021
Originaldokument gespeichert auf dem Dokumentenserver der Universität Basel edoc.unibas.ch
Genehmigt von der Philosophisch-Naturwissenschaftlichen Fakultät
auf Antrag von
Prof. Dr. Marcel Tanner (Faculty Representative)
Prof. Dr. Melissa Penny (Dissertation Supervisor)
Dr. Steven Kern (Co-Referee)
Basel, 21 Mai 2019
Dekan
Prof. Dr. Martin Spiess
Table of Contents
Mathematical modelling and analysis to understand interventions against
malaria in humans ... 1
Table of Contents ... i
Acknowledgments ... v
Summary ... ix
Chapter I ... 1
Introduction ... 1
Malaria transmission and current intervention strategies ... 2
The role of mathematical modelling ... 9
How modelling of vaccine implementation guided policy recommendations ... 16
Potential for further modelling and research ... 19
Objectives and outline of the thesis ... 23
References ... 28
Chapter II ... 33
Incidence and admission rates for severe malaria and their impact on mortality in Africa ... 33
Abstract ... 35
Background ... 37
Methods ... 40
Results ... 52
Discussion ... 67
Conclusion ... 72
Declarations ... 73
References ... 74
Supplementary material ... 77
Chapter III ... 99
Mass campaigns combining antimalarial drugs and anti-infective vaccines as seasonal interventions for malaria control, elimination and prevention of resurgence: a modelling study ... 99
Abstract ... 101
Background ... 103
Methods ... 107
Results ... 115
Discussion ... 133
Conclusion ... 142
List of Abbreviations ... 143
Declarations ... 143
References ... 145
Supplementary material ... 148
Chapter IV ... 171
Proteome-wide humoral immunity of Tanzanian volunteers immunized with an attenuated whole sporozoite vaccine reveals personalized antibody profiles ... 171
Abstract ... 173
Introduction ... 174
Results ... 181
Discussion: ... 201
Declarations ... 222
References ... 225
Supplementary material ... 232
Chapter V Mechanistic within host models of the asexual P. falciparum infection: a review and analytical assessment ... 247
Abstract ... 249
Background ... 251
Methods ... 257
Results ... 264
Discussion ... 288
Conclusions ... 299
List of abbreviations ... 306
Declarations ... 306
References ... 307
Supplementary material ... 312
Chapter VI ... 353
Discussion ... 353
Summary ... 355
Data and future clinical trials to inform models ... 361
Implications of the findings for population modelling of interventions against malaria in humans ... 370
Implication of the findings for policy recommendations ... 374
Suggested further data analysis and modelling to assess new tools ... 382
General conclusion ... 388
References ... 390
Acknowledgments
This thesis was funded by Swiss National Science Foundation through SNSF Professorship of Melissa Penny (PP00P3_170702).
First and foremost, my sincerest thanks go to my supervisor Melissa Penny.
I thank her for her support and guidance for the last years, for her advice and encouragements, for giving me the opportunity to attend several conferences, meetings, and courses to pursue my professional development, and for including me in many discussions of broader focus than my thesis. I also thank her for always being open to discuss my future career plans. She has taught me more than I could ever give her credit for here. She has been a great supervisor, mentor, and friend.
I would like to express my sincere gratitude to Tom Smith, for accepting me for an internship in his unit during my bachelor’s degree. I might have never gone down this path if I wasn’t given this opportunity in the first place. I am grateful for all the discussions we had, for his inspiring work and immense knowledge, for managing such a wonderful team, and for showing us, by his example, what a good scientist should be.
I am especially indebted to Marcel Tanner, for his guidance and advice throughout the PhD.
To my colleagues and former colleagues in the Infectious Disease Modelling Unit: Thomas Smith, Emilie Pothin, Soledad Castaño, Nakul Chitnis, Melissa Penny, Andrew Shattock, Adrian Denz, Tamsin Lee, Mar Velarde, Clara Champagne, Christine Bürli, Lydia Burgert, Lea Multerer, Emmanuel Bakare, Munir Winkel, Mirjam Laager, Nadja Cereghetti, Peter Pemberton Ross, Nicolas Maire, and my office mates Katya Galactionova, Theresa Reiker, Manuela Runge, Daniel Cobos, Nadia Pillai, Alyah Karim, and Irene Köpfer. This group is truly inspiring, and it has been a pleasure to work with you.
I am grateful to all of those with whom I have had the pleasure to work with during this and other related projects, in particular I thank Claudia Daubenberger, Joe Campo and his colleagues at ADI, collaborators at Ifakara Health Institute, collaborators at Sanaria, Cynthia Lee, Chris
Kern and anyone at the Bill and Melinda Gates Foundation who contributed to fruitful discussions throughout this PhD. I acknowledge all individuals who participated in the vaccine clinical trial in Tanzania.
To the Malaria Modelling Consortium: thank you to everyone I have had insightful discussions and exchanges with during the meetings.
To Tamsin Lee, Theresa Reiker, and Lydia Burgert for kindly volunteering to proof read chapters of the thesis.
Thank you to Christine Mensch, Dagmar Batra, Nora Bauer, Laura Innocenti, and Margrith Slaoui for all the administrative and logistic support.
To all colleagues and friends in the 3rd and 4th floor of Mission 21, including colleagues from the Household Economics and Health Systems Research Unit, thank you for making this working place so welcoming, for joining me for “pétanque”, ping-pong games, after-work social events, Rhine swimming, and for the fun or interesting discussions during lunch breaks.
To Emilie Pothin, Niggi Maire, Mirjam Laager, Simone Sutherland, Dylan Muir, Melissa Penny, Paola Salari, Tamsin Lee and everyone who joined the game nights: so many great memories, and what a satisfaction to have been able to eradicate a disease once during my PhD, in a board game.
Thank you to my friends, here in Basel and abroad, for all their support, and a special thank you to everyone in the Villars mountains, a place that has become my second home and has been my stress relief throughout my studies.
Finally, I am deeply thankful to my family, who have been there from the beginning, who gave me the confidence and strength to pursue any objectives I had in mind, and I am thankful for all the joy and love they give me every day.
It is impossible to thank everyone who helped me throughout the development and completion of this thesis, and I apologize in advance if I unintentionally I omitted someone.
Summary
Background
Achieving further reduction of malaria burden world-wide requires not just increasing access to existing interventions, but also alternative deployments and new tools. Moreover, efficient clinical and field development of new tools is important to tackle increasing resistance and residual transmission. However, it will be impossible to test all combinations of existing or new tools in the field, or to bring tools to the field without substantial understanding of individual responses and their potential impact on overall mortality or other health metrics. Thus, this PhD research employed modelling and analysis for predictions at population level down to analysis of clinical studies and within host dynamics to understand interventions in humans, burden statistics, and thus the role of models and their assumptions in assessing new tools.
Methods
Several computational approaches were used throughout the thesis. Specifically, this thesis:
i) Estimated country specific access to in-patient care and
incidence of severe cases by combining individual-based models of malaria transmission dynamics together with data, such as national reported statistics on burden. These models were further used to estimate the impact of improving access to in- patient care on mortality;
ii) Investigated the potential impact of mass interventions using drugs and anti-infective vaccines in elimination strategies via analysis of a large numbers of simulation results in order to support interpretation of likely impact before trials. Across a range of malaria settings, intervention coverages and
deployment frequencies, simulation results were analysed for several epidemiological outcomes, namely prevalence reduction,
chance of interruption of transmission, delay of resurgence, and synergism between drugs and vaccines;
iii) Assessed the antibody responses in malaria pre-exposed individuals immunized with the PfSPZ Vaccine in Tanzania via statistical analysis of whole protein microarray data from three cohorts in a clinical study. The humoral immune response pattern in regards single proteins or breadth of responses before and after immunisation in non-vaccinated and vaccinated were compared;
iv) Reviewed and analysed simulation outputs of several mechanistic within host mathematical models of parasite dynamics to understand their assumptions concerning blood stage parasite growth and host immune responses, which potentially impacts predictions of efficacy of within-host interventions.
Results and significance
Mortality estimates are sensitive to assumptions of access to treatment of clinical and severe-cases, and current reported data highlights large variation in levels of access to in-patient care across Africa. These results have implications on current approaches to estimate mortality statistics from reported data, which also form the basis of assessing new tools in policy decisions. In the context of elimination strategies, simulations suggest that potential synergism between drugs and vaccines can lead to a rapid decrease in malaria prevalence, delay malaria resurgence, and in some settings interrupt transmission. The substantial delay in resurgence predicted when a vaccine is delivered with a drug is a new observation from my study.
Personalized breadth of immune responses in pre-exposed individuals plays a role in vaccine take and protection which is not yet fully understood. This suggests further research on personalized responses to vaccines is required during clinical development. Finally, current mechanistic within host models of blood stage asexual parasite
infections include large inter-individual variations, at the same time recent findings on parasite growth rates in vivo and on variant gene expression challenge model assumptions around both growth rates and parasite-immune effectors.
Conclusion
Modelling and statistical analysis plays an increasingly important role to understand and assess interventions within an individual, and across populations. Such approaches, only informed or combined with data, can help fill knowledge gaps before moving to costly clinical and field trials, generate hypothesis on alternative use of interventions and likely benefits to be observed, or estimate which outcomes could be monitored in trials. Credible use of these models and analyses depends on understanding their assumptions. Improved estimates of disease burden in malaria endemic countries are required to support targeted development of new tools and allow assessment of likely impact and cost- effectiveness for policy decisions.
Chapter I
Introduction
Malaria transmission and current intervention strategies
Transmission and parasite life cycle
Malaria is caused by the Plasmodium parasite, of which five different species can infect humans. Plasmodium is a unicellular protozoan and the most prevalent and deadly of all is Plasmodium falciparum, which is the primary species in Africa and is the focus of this thesis. Another species causing disease in humans is Plasmodium vivax, which is prevalent in Asia and South America [1]. Plasmodium falciparum is a vector borne disease and the parasite in different forms resides in two hosts: mosquitos and humans. The parasite’s full life cycle implies sexual reproduction in the mosquito and asexual reproduction in humans. The infection in humans begins when an infectious female Anopheles mosquito bites a human, and the parasite, in its sporozoite form, is
transferred from mosquitos to human skin. These sporozoites migrate from the skin to the blood and quickly reach the liver, where they invade liver cells. Following a hepatic cycle of approximately 5-10 days in the liver, the parasites, now in merozoite form, leave the liver and are released into the blood stream, where they invade and sequester red blood cells to undergo replication with a 48-hour life-cycle [2]. Clinical symptoms and
parasites differentiates into gametocytes, a sexual parasite form, which are ingested by mosquitos, subsequently undergoing sexual reproduction and later ready to infect a new human host.
In order for this Apicomplexa to reside in these different hosts and cells, it is divided in several life stages, such as the sporozoite stage in the mosquito vector, schizont in the liver, or merozoite, trophozoites, schizonts and gametocytes in the blood stream. Theories around the origins of human malaria diverge [3], but in general malaria is thought to have begun infecting humans over ten thousands of years ago. Thus, the human malaria parasite has co-evolved with humans. The long-term cohabitation between the parasite and human host, and the complexity of the parasite’s life stages has made it difficult for humans to build natural immune responses leading to sterile protection. Theoretically, one can fight malaria by using interventions which act at different stages, such as acting on the mosquito stages with insecticides, larviciding, or insecticide treated nets, preventing the infection at human liver stages with anti-infective vaccines or drugs, preventing disease or curing infection at blood stage with drugs or anti-disease vaccines, or preventing the transmission back to mosquitos with
transmission-blocking drugs or vaccines. This thesis is focused on analysing and modelling interventions acting on human parasites
and not interventions acting on vectors or parasites within the vector.
Malaria burden and goals for the near future Between 2000 and 2015, malaria burden experienced a great decline. Global efforts to reduce malaria burden led to an estimated 20% reduction in the number of malaria cases, from 262 million cases in 2000 to 214 million in 2015 [4], and an almost 50% reduction worldwide in the estimated malaria deaths, from 839,000 in 2000 to 438,000 in 2015 [4]. This progress is mainly attributed to large scale deployment of vector control consisting of insecticide treated nets and indoor residual spraying, but also to the rollout of, and improved access to, artemisinin-based combination therapy [5]. This positive decline in malaria as a result of
implemented control measures motivated the World Health Organization (WHO) to define and update its Global Technical Strategy (GTS) for Malaria for the years 2016-2030 [6]. The GTS set goals for both elimination and mortality reduction, aiming for a reduction of malaria incidence and deaths of 40% and 90% by 2020 and 2030, respectively, compared to numbers in 2015, and to eliminate malaria from at least 10 and 35 countries by 2020 and 2030, respectively [6].
However, recent burden estimates in 2017 and 2018 indicate that continued progress in malaria burden reduction is threatened [7]. Indeed, the World Malaria Report published in 2017 reported that progress had stalled, and furthermore malaria endemic countries faced major challenges such as sustained international and domestic financing, political commitment and parasite and mosquito resistance [7]. The latest report from WHO in 2018 worryingly reported uncertainty in the world achieving the GTS goals by 2020 and confirmed malaria incidence had increased in the ten highest burden countries compared to the estimates reported in 2017 [1]. It is clear that existing vector control and drug interventions currently in use and delivered via existing health systems will not be sufficient to eliminate the disease in many settings let alone eradicate globally. It is likely that the last steps towards malaria elimination will be a complex task technically, financially, and politically. Thus, much effort will be needed to scale up coverage of existing interventions, and there is a pressing need for new tools to be on the market [6]. New tools and
intervention strategies are required to tackle drug resistance, insecticide resistance, residual transmission such as outdoor transmission, and to address coverage gaps. In this thesis, a tool refers to the drugs, vaccines, or vector control such as insecticide treated net, and tool properties refer to the efficacy or durability of
the tool. An intervention generally refers to the use of the tool, such as a mass vaccination or case management, and intervention properties refer to coverage of the population and/or effectiveness across a population.
Funding
In 2017, funding for malaria control and elimination was estimated at US$ 3.1 billion, with contributions from endemic countries (almost 30%), international countries, with US being the largest international source of financing (30%), and contributions from the Bill & Melinda Gates Foundation (BGMF) (2%) or other donors [1]. On the research and development side, international efforts to accelerate development are facilitated by several public and private contributions, with public financing mainly focused on basic research, and philanthropist like BMGF, non-profit global health organization like PATH, or public-private partnership such as Medicine for Malaria Venture mainly focused on applied research for new tools and implementation [1]. Despite these efforts, no vaccine and only a limited number of new drug
compounds have reached the market. A few vaccines have reached or are reaching Phase III clinical trials [8] and many drugs are in Phase IIb or late stages of clinical development [9]. Decisions on
implementation will be challenging. Although not standard, decisions are sometimes informed by the predictions of the tool’s cost effectiveness and impact on deaths.
Current strategies
Despite 50% reduction in malaria mortality compared to 2000, in 2017 an estimated 435,000 people died from malaria, making malaria the fourth highest global burden from infectious diseases after respiratory infections (including tuberculosis and other pathogens), enteric infections (caused by several pathogens), and HIV/AIDS [10]. Most of these malaria deaths were in children under 5 years old in Africa [1]. Given progress in malaria reduction has stalled, now is a critical time in which efforts are needed to maintain gains and achieve further reductions of malaria burden.
As such, the GTS established 3 pillars supporting their defined goals which also apply to reversing stalled progress. These pillars are: i) ensuring universal access to malaria prevention, diagnostics and treatment, ii) accelerate efforts towards elimination and iii) increase malaria surveillance [6]. Supporting these pillars, the need to expand research and development was highlighted as essential to increase innovation and to rapidly test and implement new tools to benefit from innovations in therapeutics and vector control tools over the last decade [6].
In 2018, WHO announced a new initiative called High Burden to High impact [11], aiming to decrease malaria in countries with highest burden [11]. For these countries, WHO encourages not only greater involvement by the country
themselves, with a country-led malaria response thus pushing for ownership and political will to reduce malaria deaths, but also better guidance, policy and strategy [11]. Guided by the GTS, the Global Malaria Program (GMP), which is coordinating WHO’s efforts to control and eliminate malaria, are about to update their GMP policy pathway to facilitate the impact of new tools and strategies [12]. In particular, they highlight that policy
recommendations for new tools and strategies, which usually rely on the predicted impact on life saved and on cost effectiveness estimates, should be facilitated and accelerated by anticipating the potential addition of new tools and strategies and thus framing the policy recommendation early in the development pipeline [12], at a stage where direct estimates on deaths averted and cost
effectiveness cannot be made and model-based predictions are needed. This is particularly important when considering the timelines for novel therapeutic development, in particular for vaccine development. As illustration, development of
RTS,S/AS01, the world’s first vaccine against a human parasite, involved massive efforts lasting more than 20 years between the
first clinical trial in Africa in 2001 assessing the efficacy of RTS,S against natural infection in Gambian adults [13], to the future evaluation of the pilot implementation of RTS,S expected in 2022 [14]. Furthermore, malaria vaccine research started in the 1960’s with the Circumsporozoite Surface Protein (CSP), the
immunodominant antigen used in RTS,S only cloned and sequenced in the 1980’s, more than 35 years ago.
The role of mathematical modelling
History
Mathematical modelling in infectious diseases can serve several purposes. The complexity of malaria, both at intra-host and inter-host level, leads to transmission dynamics and impact of interventions that are difficult to assess intuitively or via observation or data only. Mathematics provide a framework to formally describe those dynamics, evaluate the quantitative or qualitative role of each of the components of the transmission model, and predict the impact of interventions [15], [16]. At a population level, the concept of applying mathematical modelling to describe malaria transmission dynamics began last century with Ronald Ross, who, after identifying the role of mosquito as a
vector for malaria, described malaria transmission using
“pathometry” which he defined as the use of mathematics for epidemiology [17].
The original model of Ronald Ross is a combination of a so-called Susceptible – Infectious – Susceptible (SIS)
compartmental model for the dynamics in humans, where humans move between two states, infected or susceptible, and a Susceptible – Infectious (SI) model for the dynamics in mosquitos where the mosquitos, once infected, are assumed to stay infected (and die due to their short life-time). The model describes the transmission dynamics for mosquitos and humans to become infected, which involves mosquito and human related parameters such as the biting rate, the proportion of bites resulting into an infection, or the recovery rate of a human once infected [18]. By including the mosquito population, this model was the first formal description of the vector’s contribution to malaria transmission.
Ross’ model was extended fifty years later by Georges Macdonald [19] and remains the basis of many recent models [18].
Ross-McDonald model is a compartmental model described via ordinary differential equations, and includes latent exposure of mosquitos. The Ross-McDonald model motivated mosquito based
theoretical justification of the large scale DDT insecticide spreading during the Global Malaria Eradication Programme between 1955 and 1969 [20].
In the early 1970’s, the Garki project led by WHO took place in a region in Nigeria to gain quantitative knowledge on malaria transmission and impact of control interventions [21].
Extensive observations and field measurements taken during the project covered numerous aspects related to malaria transmission such as meteorological, entomological, parasitological, and immunological measures, and several control tools such as
insecticides or drugs were assessed. Importantly, the assessment of mathematical models was a significant component of the trial, to test if mathematical modelling calibrated to field data could predict intervention outcomes and thus be of use for rational control programme planning [21]. The Garki model was developed by Dietz et al [22] and included novel concepts such as different stages of immunity to account for the partial immunity gained if exposed, with the infectious and recovery rates dependent on human immunity levels [22].
From a historical perspective it is worth noting that, not only did Ronald Ross discover the importance of mosquitos for malaria transmission and contribute substantially to the role of
infectious disease modelling in general, he also published on the important principle of intervention combination. Ronald Ross stated that it was very unlikely that a single intervention could reach coverages high enough to interrupt transmission, and thus malaria elimination strategies should include more than one intervention, targeting both the vector and human component of malaria and adapting the interventions to be feasible in local settings [23]. It is in this context that we discuss the role of malaria modelling of the last decade, namely individual based models, to understand single tools or intervention combinations.
The role of individual based models
Compartmental mathematical models in the form of SIS or similar are useful to understand transmission dynamics and to identify key dynamics for intervening in homogenous populations.
As mentioned in the previous section, the Dietz et al [22] model used in the Garki project included different immune states for humans. However, to expand such a model to include immunity dependent on the age of the host and pre-exposure leads to
complexities difficult to capture with such models. More generally, any heterogeneity such as heterogeneity in transmission dynamics, individual heterogeneity in responses to tools or immunity, or
model via SIS models, as for each component complexity is added.
Thus, complementary to using and further developing those models, the twenty-first century was a turning point in the malaria modelling community, with the development of individual based malaria models.
Individual based models track outcomes for each individual, such as infections, disease state, mortality etc., compared to SIS models where the output is per population and thus an average across the population. Heterogeneity in individual outcomes is of particular interest when the distribution of outcomes across the population is more important than the average outcome [24]. In addition, the Global Malaria Eradication Program of the 1950’s resulted in drastic malaria reduction and elimination in some places, but also failure in many areas, highlighting the complexities of malaria dynamics, and indicating that in some countries elimination might be a difficult target. This resulted in a switch in targets from eradication to burden reduction. At that time, mathematical models were focused on understanding transmission dynamics and elimination, but much less on the dynamics leading to malaria mortality and the heterogeneous impact of interventions on transmission, clinical cases or mortality reduction. For burden reduction, models need to not only be capable of predicting interruption of transmission or prevalence reduction, but also be
able to predict cases averted, deaths averted, or other health
metrics. Deaths can be considered a fairly rare event (relative to the high number of asymptomatic malaria infections, and malaria cases not leading to death), and for modelling rare events individual based models are a practical tool [25].
Brief overview of OpenMalaria, an individual- based simulation model of malaria transmission and control
In 2003-2006, an ambitious project was undertaken to create the world’s first malaria individual based model [24]–[27].
OpenMalaria [28] was created, pushing mathematical modelling of malaria beyond describing transmission and potential transmission interruption to that of predicting mortality, the role of interventions in malaria control and burden reduction, and cost-effectiveness analysis [29]. OpenMalaria places at the centre of the model individual level tracking of the asexual parasite densities.
Individual infections are variable in length and parasite density levels, and subsequent clinical disease or onwards transmission to mosquitoes driven by those individual levels. The asexual stage in this model has three critical roles, first the gametocytes generated at this stage enable the infection to be transmitted to the mosquito taking a blood meal, second parasite density levels determine
parasite densities vary both between population age groups and the geographic transmission intensities due to partial immunity resulting from pre-exposure. The Entomological Inoculation Rates (as a direct input or determined by the vectorial capacity) are inputs and drivers of initial transmission in the model, from which the model simulates corresponding infection of each human host, the asexual parasite densities, and the infectivity to the mosquito [24]. Clinical episodes, severe episodes, and deaths occur as the result of asexual parasite density [24]. These processes are stochastic by nature, vary with natural immunity from pre- exposure, and can be modulated by adding the effect of a given intervention and given levels of case management [24].
In contrast to previous models that were generally differential equations, OpenMalaria is modular with several statistical and mechanistic models to describe processes inherent in each individual, e.g. probability of being bitten by an infected mosquito for a given vectorial capacity or if under an intervention, probability an infection, empirical model for parasite densities, immunity acquisition and effect on disease, pathogenesis, exposure, vector dynamics, and more. The model has a formal description fully detailed in publications or online [24]–[28].
Originally OpenMalaria was developed to understand the potential impact and cost-effectiveness of a vaccine, and subsequent to 2006
significant improvements in the model in regards vector models, transmission heterogeneity assumptions, immunity decay, have been added. OpenMalaria, along with several other individual based models developed since 2007 (see for example model from Imperial College [30], or EMOD [31] ), are all models that have been used to guide WHO policy recommendation over the last decade (see for example consensus modelling on MDA
programmes [32], on RTS,S/AS01 [33], or modelling exercise for vector control scale back [34]), or to support national malaria control programs (example for elimination strategies in Zambia [32], [35] ).
How modelling of vaccine implementation guided policy recommendations
The most advanced vaccine, RTS,S/AS01, an anti- infective vaccine based on the Circumsporozoite Surface Protein (CSP), completed Phase III clinical trials in 2015 [36].
RTS,S/AS01 received a positive scientific opinion from the European Medicine Agency [37], and WHO’s recommendation following the EMA decision was for additional pilot studies rather than wide-scale implementation in Africa [38]. RTS,S/AS01 pilot
implementation in three African countries begins at the time of submission of this thesis and coincides with the world malaria day 24th April 2019. The RTS,S/AS01 Phase III clinical trial lasted 5 years and involved more than 15’000 children or infants in seven sub-Saharan African countries of different transmission intensities.
This trial could necessarily only assess efficacy of immunization within the constraints given by the trial, such as a limited range of transmission intensities and high case management levels. The trial’s endpoint was primarily to assess the effect of immunization on the number of clinical and severe cases, based on a cohort including 2 age groups, infants age 6-12 weeks and young children age 5-17 months, monitored for 5 years. As with most vaccine Phase III studies, the ability the assess the impact on mortality was limited by power, despite the large trial.
Extensive efforts have been undertaken to be able to predict impact of the vaccine beyond the trial settings and beyond outcomes of clinical and severe disease (examples in [33], [39], [40]). A joint effort from four different modelling groups, including Swiss Tropical and Public Health Institute using OpenMalaria, provided model-based evidence to the WHO Strategic Advisory Group of Experts (SAGE) [41] and the WHO Malaria Policy Advisory Committee (MPAC) [42]. The modelling groups concluded that RTS,S/ AS01 implementation would likely
have a positive public health impact and has potential to be cost- effective in moderate transmission settings depending on metrics of comparison [33]. The predictions from these modelling groups used malaria transmission models parameterized to extensive trial data in regards clinical efficacy and/or individual immune
response, thus estimating protection levels at individual level. Each model fitted time-courses of vaccine efficacy against infection [33], [43], [44] to be able to estimate via their models the impact at population level. This modelling exercise allowed for predictions from low to moderate to high transmission settings, predicting the impact of RTS,S/AS01 immunization on prevalence, mortality and other health metrics, as well as cost-effectiveness, for the entire population, and up to 15 years following start of implementation [33].
As this work was a WHO coordinated exercise, this collaboration played a role in guiding thinking in WHO’s recommendation concerning RTS,S/AS01 [38]. This modelling exercise is an example of how modelling can play a role in policy recommendations. The collaboration between WHO and modelling groups is continued today during the preparation and future
evaluation of the malaria vaccine pilot implementation [45].
In addition to the analysis for WHO, extensive efforts were made to predict cost effectiveness of RTS,S/AS01 via routine immunization for 43 sub-Saharan African countries, giving insight to potential benefits of RTS,S/AS01 immunization given current country settings [39]. Sub-national level predictions of public health impact of RTS,S/AS01 were also provided to GAVI, the Vaccine Alliance as part of their vaccine investment strategy reviews. GAVI, the Vaccine Alliance support commodity costs for immunization of a wide range of diseases in developing countries.
During the time of my PhD thesis I was involved in providing estimates to GAVI for vaccine impact and providing estimates of vaccine impact against clinical disease and mortality that were used in the cost-effectiveness calculations of Galactionova et al [39]. I am a co-author on the cost-effectives paper [39], but this work is not reported in this thesis.
Potential for further modelling and research
In general, public health impact estimates of a given intervention are reported as impact on mortality in order to compare to other interventions. These estimates can have
implications for policy recommendations or investment decisions as exemplified in the previous section: GAVI the Vaccine Alliance
compared mortality impacts across vaccines to guide decisions on which vaccines to fund, including malaria. Despite the importance of these predictions in averting mortality, estimates of mortality burden remain uncertain for many diseases, including malaria. At present, there is much uncertainty regarding malaria mortality rates, and underlying severe incidence of malaria. Existing models have been fitted to limited and imprecise data on severe disease and mortality [46], [47]. This has implications on the credible use of the models predicting the impact of an intervention on mortality burden. Furthermore, even though burden statistics and
methodologies improve [1], [48], outputs from models which estimate the yearly global and country burden statistics for WHO are also uncertain. This is particularly relevant in the context of innovation and assessing new tools, and even for the continued assessment of RTS,S/AS01. The impact evaluation of the pilot implementation of RTS,S/AS01 specifically intends to assess the impact on all-cause mortality, malaria mortality, and malaria hospitalization rates [14], all of which are difficult metrics to observe or estimate.
In the context of innovation and combination interventions, malaria vaccines could be considered as tools beyond mortality reduction, and modelling plays a role to assess such needs prior to
largely focused on the paediatric routine immunization to protect and reduce mortality in the population group most at risk, as to date this has been the primary therapeutic age target. In addition to burden reduction, which has an unquestionably high importance, WHO is aiming for malaria elimination in the future, and alternative strategies for RTS,S/AS01 or future vaccines in the context of elimination are being suggested [49], [50]. Previous modelling studies investigated the role of hypothetical vaccines for burden reduction and interruption of transmission [51], and
predicted the potential role of an anti-infective vaccine for
elimination only with very high coverage level, in low transmission settings and with a vaccine with a long duration efficacy [51]. It is clear that the current tools and the tools in the near future,
including vaccines, will not have the sufficient properties to tackle malaria elimination on their own, the key being to combine the optimal sets of tools. Modelling and simulation can help assess different combinations of interventions, for example vaccines with drugs, and help understand the dynamics leading to prevalence reduction and elimination or malaria resurgence.
RTS,S/AS01, and most likely all advanced malaria vaccines in the development pipeline, confer limited duration of protection, with the mechanisms leading to protection and immune response induced by the vaccines not yet fully understood [52].
Unfortunately, developing a vaccine for malaria presents a greater challenge than for other pathogens that confer sterile immunity after exposure, for example measles or smallpox. And even though smallpox vaccination was paramount to smallpox eradication [53], a full understanding of variola virus and its interactions with human host immunity was not needed for vaccine development.
This will not be true for malaria, as the malaria parasite has over 5,000 proteins, many different life stages residing in different hosts, organs and cells, and does not naturally induce sterile immunity, with only partial immunity observed in exposed population [53]. Thus, with advances in technologies and
experimental methods, basic research must continue and is needed to understand individual immune responses to exposure or induced via malaria vaccines when tested, in order to understand the potential effect of vaccination on a given population and the resulting public health impact.
The inter individual variability in the parasite densities is a commonly accepted fact, and was first observed in historical experimental data in naïve individuals about fifty years ago [54].
Many within host models of parasite dynamics have addressed this question, and the models have been imbedded in transmission models [55]–[58], but a review and comparison of those models
of the biological dynamics of asexual parasite growth has not yet been published. As described in previous sections, the asexual parasite density is a major component influencing both malaria transmission and clinical symptoms or death. In addition, several blood stage tools such as drugs or potential vaccines, can modify those dynamics. The asexual parasite dynamics are particularly important when a given tool has partial efficacy, thus only partially reducing parasite growth instead of clearing or preventing the infection. This can be a consequence of the tool’s limited efficacy (blood stage vaccines), or it can be result of sub-optimal doses (low adherence in drug take), or partial parasite resistance. In this context, within host models currently used should be assessed to understand their role and limitations for predicting drug or vaccine efficacy at an individual or population level.
Objectives and outline of the thesis
Through a multi-level approach using mathematical modelling and analysis, this thesis aims to address several important aspects of malaria burden, dynamics, and malaria vaccines. Specifically, the thesis aims i) to investigate malaria burden statistics and how they are estimated, and thus the role of models and their assumptions in assessing new tools; ii) to perform
in silico simulations exploring the potential role of anti-infective vaccines for elimination; iii) to deepen our understanding of the parasite-human interactions analysing the immune response in pre- exposed or immunized populations; and iv) to assess current mathematical models of blood-stage asexual parasite dynamics.
Thus, the thesis is divided into the four following objectives:
i) To estimate country specific access to in-patient care and incidence of severe malaria cases in 41 sub-Saharan African countries, to estimate the derived mortality rates, and to assess the impact on mortality of improving access to care;
ii) To investigate the potential impact of drug and anti- infective vaccines beyond paediatric indications to
interrupt transmission or delay malaria resurgence, in order to support interpretation of likely impact before trials;
iii) To analyse the humoral immune response against the whole parasite proteome in malaria pre-exposed individuals immunized with the PfSPZ Vaccine;
iv) To assess simulation outputs of several mechanistic within-host models of parasite dynamics, and compare their assumptions on parasite growth and host immune responses.
The first objective aims to understand the current burden of malaria in endemic African countries. Malaria mortality is driven by severe disease, yet severe incidence and mortality are both difficult to measure in the field or monitor through
demographic health surveys. Via simulation-based modelling and using available data, estimates from the World Malaria Report, Malaria Atlas Project and other publications, two different
prediction approaches were developed to estimate the incidence of severe cases and the level of access to in-patient care of severe cases in 41 sub Saharan African countries. These modelled estimates were then used to extrapolate mortality reduction possible in each country if in-patient access for severe cases was increased to 100%. This chapter proposes several approaches to estimate severe incidence and in-patient access, highlighting large variability in both reported data and estimated burden statistics.
This chapter has implications for global burden of disease methodologies, highlighting the need for better data and better approaches to estimate mortality and severe disease.
The second objective focuses on the potential use of vaccine in combination with other interventions, namely mass administration of drugs and mass immunization with anti-infective vaccines as interventions to reduce malaria prevalence in highly seasonal settings. Using a simulation-based approach with
OpenMalaria [28] this chapter reports estimated prevalence reduction following deployment of interventions exploring different delivery schedules, intervention coverage levels, importation rates, transmission intensities and case management settings, thus exploring many more scenarios than can be possibly investigated in field trials. Prevalence reduction, chances to interrupt transmission, resurgence dynamics, and synergistic behaviours are the primary outcomes investigated. This chapter serves to guide thinking for policy makers and future clinical trials, in regards deployment strategies of the combined tools that might warrant investigation, and in which setting and for what purpose.
In addition, this chapter highlights that soon to be available and/or existing tools in combination targeting different aspects of the malaria life-cycle may have additional benefits not previously identified. This includes delaying resurgence following mass interventions so that surveillance response strategies may be more efficient. The study was conducted in collaboration with PATH’s Malaria Vaccine Initiative.
The third objective aims to investigate the humoral immunity in pre-exposed individuals, and its role in the vaccine take. Using whole proteome microarray data analysis, this chapter analysed the entire spectrum of humoral immune response before
vaccine and challenge study [59]. In this chapter the potential association between humoral immune responses and protection against Controlled Human Malaria Infection (CHMI) following PfSPZ immunization were investigated and discussed. This chapter provides additional evidence to support understanding of patterns of humoral response in a pre-exposed population before and after immunization, and thus support immunity understanding and future vaccine development. This study involves collaborations with several partners: Ifakara Health Institute, who tested the vaccine, the vaccine developer Sanaria, Antigen Discovery Inc., who performed the microarray experiments and contributed expertise on microarray data analysis, and the Clinical Immunology group at Swiss TPH.
Finally, the last objective of this thesis focuses on
understanding existing mechanistic within host models of asexual parasite dynamics at the blood stage. Through a review and analytical assessment of several models from the literature, this study aimed to identify key features and limitations of current within host models. This chapter reports and discusses the simulated and assessed mechanistic models and their dynamics describing parasite growth and the effect of the modelled immune responses and other host factors. The purpose of this chapter is to provide understandings of benefits and limitations in using these
models to assess drugs or vaccines, or as a starting point to develop new models.
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Chapter II
Incidence and admission rates for severe malaria
and their impact on mortality in Africa
Flavia Camponovo1,2,§, Caitlin A. Bever1,2,3,§, Katya Galactionova1,2, Thomas Smith1,2, Melissa A. Penny1,2*
1 Swiss Tropical and Public Health Institute, Basel, Switzerland
2 University of Basel, Basel, Switzerland
3 Current address: Institute for Disease Modeling, Bellevue, WA 98005, USA
§ These authors contributed equally to this work
This paper has been published in Malaria Journal vol. 16, no. 1, Dec. 2017, https://doi.org/10.1186/s12936-016-1650-6
Abstract
Background Appropriate treatment of life-threatening Plasmodium falciparum malaria requires in-patient care. Although the proportion of severe cases accessing in-patient care in endemic settings strongly affects overall case fatality rates and thus disease burden, this proportion is generally unknown. At present, estimates of malaria mortality are driven by prevalence or overall clinical incidence data, ignoring differences in case fatality resulting from variations in access. Consequently, the overall impact of preventive interventions on disease burden have not been validly compared with those of improvements in access to case management or its quality.
Methods Using a simulation-based approach, severe malaria admission rates and the subsequent severe malaria disease and mortality rates for 41 malaria endemic countries of sub-Saharan Africa were estimated. Country differences in transmission and health care settings were captured by use of high spatial resolution data on demographics and falciparum malaria prevalence, as well national level estimates of
effective coverage of treatment for uncomplicated malaria. Reported and modelled estimates of cases, admissions and malaria deaths from the World Malaria report, along with predicted burden from simulations, were combined to provide revised estimates of access to in-patient care and case fatality rates.
Results There is substantial variation between countries’ in- patient admission rates and estimated levels of case fatality rates. It was found that for many African countries, most patients admitted for in- patient treatment would not meet strict criteria for severe disease and that for some countries only a small proportion of the total severe cases are admitted. Estimates are highly sensitive to the assumed community case fatality rates. Re-estimation of national level malaria mortality rates suggests that there is substantial burden attributable to inefficient in- patient access and treatment of severe disease.
Conclusions The model-based methods proposed here offer a standardized approach to estimate the numbers of severe malaria cases and deaths based on national level reporting, allowing for coverage of both curative and preventive interventions. This makes possible direct comparisons of the potential benefits of scaling-up either category of interventions. The profound uncertainties around these estimates highlight the need for better data.
Background
Each year the prompt and effective treatment of
Plasmodium falciparum malaria saves the lives of children across malaria endemic countries. Recent analysis has estimated that scale-up of vector control (insecticide-treated nets and indoor residual spraying) and artemisinin combination therapy have reduced malaria prevalence by 50% and clinical incidence by 40%
in endemic Africa over the years 2000-2015 [1]. However, it is unclear how many deaths are prevented each year by the treatment of both uncomplicated and severe clinical malaria. Hospital case fatality rates for well-defined severe malaria are relatively well established [2], [3]. However, these do not translate directly into estimates of the impact of effective management of severe disease on malaria mortality rates, for which only estimates based on expert opinion are available [4] and, to date, there are no good estimates of how these translate into numbers of malaria deaths averted.
The World Health Organization’s annual World Malaria Report (WMR) [5] provides information on country-specific numbers of cases and admissions reported by national Health Ministries. The admission rates vary enormously, raising the question of whether they can be interpreted in the same way for all
countries. These statistics make no distinction between different levels of disease severity, and it would appear that in many countries large numbers of uncomplicated malaria patients are admitted as in-patients to health facilities.
The inclusion of uncomplicated malaria patients in
statistics on malaria admission means that these numbers cannot be used uncritically to estimate the proportion of severely ill people that access such care. In general, population-based estimates must be used to estimate access rates, and recent Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS), have made much more data available on access to care for malaria [6]–
[8]. These data demonstrate enormous variations between countries in access to treatment for uncomplicated malaria [9], but such surveys do not provide good estimates of severe malaria incidence in the community because it is a relatively infrequent acute disease, unlikely to be encountered at the exact time of a household visit, and cannot be reliably diagnosed from reported signs and
symptoms. There are consequently no good direct estimates of the numbers of severe episodes in endemic countries that fail to access appropriate care. Several studies have found no better source of information on this than the 1996 review of McCombie [10], which is methodologically limited and now very outdated. Goodman et
average 48% (with high and low estimates of 19% and 88%) of severe malaria cases in the sub-Saharan region are admitted, and several models have continued to use this value (or similar constant values [12]) in the absence of any more reliable source [13], [14].
There are thus gaps in routine statistics on overall
incidence of severe disease, on the corresponding care-gap, and its public health consequences. To contribute to filling these gaps this study proposes model-based methods to estimate the number of severe malaria cases occurring in each malaria endemic country in sub-Saharan Africa, the proportion admitted to in-patient care, and the corresponding public health burden. These methods rely on available national or geographic reported clinical and treatment data, as well as available risk and exposure information for each country. Specifically, the estimates are based on the following data or model inputs: (i) estimates of the distributions of transmission intensities based on the prevalence data assembled by the Malaria Atlas Project (MAP) [15]; (ii) the effective coverage of treatment for uncomplicated malaria, estimated from survey data [9]; (iii) national level reports of numbers of in-patient deaths and estimates of total deaths from WMR [5]; (iv) models for severe disease incidence as allowing for the effects of treating uncomplicated disease, and calibrated by triangulating the relationships between severe disease, mortality, and transmission intensity [13]. By
applying published estimates of case fatality rates among both in- patients and in the community we estimate the numbers of clinical episodes and deaths averted by case management of both
uncomplicated and severe malaria and use these estimates to project the current burden of malaria in Africa. These estimates additionally indicate the public health impact that could be achieved by improving access to appropriate care for severe disease.
Methods
Data sources and notation
For each of the 41 countries in sub-Saharan Africa for which sufficient data was available, estimates for 2014 of the average incidence rate of both uncomplicated (U) and severe (S) clinical malaria, and the malaria specific (direct) mortality rate (D), were collated. In each case the estimates were disaggregated according to whether the event was as an in-patient (subscript h) or in the community (subscript c) or both combined (subscript t). The estimates were obtained from two sources, either as reported in WMR, denoted by accent ", or calculated from simulation models
