clinical manifestations of arboviral diseases in Venezuela

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Epidemiology, genetic diversity and clinical manifestations of arboviral diseases in Venezuela Lizarazo, Erley F.

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10.33612/diss.108089934

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clinical manifestations of arboviral diseases in Venezuela

Erley Ferlipe Lizarazo Forero

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Department of Medical Microbiology and Infection Prevention of the University Medical Center Groningen (UMCG), University of Groningen, The Netherlands.

This work was supported by the Fondo Nacional de Ciencia y Tecnología e Innovación (FONACIT), grant 201100129, 201300201; by the Coordinación de Aplicación de Fondos e Incentivos para el Desarrollo de Planes de Ciencia, Tecnología e Innovación, Organic Law of Science, Technology and Innovation (LOCTI), certification No DGCAFIDCTI/204-214-10, of the Venezuelan Ministry of Science, Technology and Innovation, Venezuela. E. F. Lizarazo Forero received an Abel Tasman Talent Program grant of the Groningen University Institute for Drug Exploration (GUIDE) and financial support from the Department of Medical Microbiology and Infection Prevention, UMCG, University of Groningen, Groningen, The Netherlands.

The printing of this thesis was financially supported by:

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978-94-034-2168-1 (Printed book) 978-94-034-2167-4 (Digital) Cover design:

Maria F. Vincenti-González / Erley Lizarazo Cover Illustrations

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Ioana Margineanu / Erley Lizarazo Print:

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clinical manifestations of arboviral diseases in Venezuela

PhD thesis

to obtain the degree of PhD at the University of Groningen

on the authority of the

Rector Magnificus Prof. C. Wijmenga and in accordance with

the decision by the College of Deans.

This thesis will be defended in public on Wednesday 11 December 2019 at 14.30 hours

by

Erley Ferlipe Lizarazo Forero

born on 5 December 1987

in La Fría, Venezuela

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Prof. J.W.A Rossen Prof. A.W. Friedrich

Co-supervisor Dr. A. Tami Dr. M.E. Grillet

Assessment Committee Prof. P. Mayaud

Prof. M. de Jong

Prof. J.M. Smit

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Paola Lisotto

Erwin Raangs

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Table of contents 1

Chapter 1 General Introduction and Scope of the Thesis 9 Chapter 2 Spatial dynamics of chikungunya virus in Venezuela: The first

six months of the epidemic 29

Chapter 3 Concomitant chikungunya and dengue epidemic in Carabobo State, Venezuela 2014: a study on epidemiological development, clinical manifestations and risk factors

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Chapter 4 Applied shotgun metagenomics approach for the genetic

characterization of dengue viruses 89

Chapter 5 Complete coding sequences of five dengue virus type 2 clinical isolates from Venezuela obtained through shotgun metagenomics

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Chapter 6 DEN-IM: Dengue virus identification from metagenomic and

targeted sequencing data 123

Chapter 7 Evolutionary history and population dynamics of dengue

viruses in Venezuela 157

Chapter 8 Summarizing Discussion 193

Appendix Thesis summary

Nederlandse Samenvatting

Informed Consent and Survey Instruments Acknowledgments

Curriculum Vitae

210 214 218 242 246

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1 1

General Introduction and

Scope of the Thesis

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GENERAL INTRODUCTION ARBOVIRAL DISEASES

Arthropod-borne viruses (arboviruses) are pathogens causing high morbidity and mortality among humans and domestic animals worldwide (Vasilakis & Gubler, 2016; LaBeaud et al., 2011). Arboviruses were initially considered to be unimportant causative agents of human dis- ease (Wilder-Smith et al., 2017). Nowadays, they are recognized as public health and biosecurity threats due to their continued emergence and re-emergence in the last five decades (Gubler, 2002). More than 100 arboviruses are known to cause disease in humans, including members of the Flaviviridae, Bunyaviridae, and Togaviridae families. Arboviruses are transmitted to humans by the bite of an infected arthropod, predominantly mosquitoes and ticks. The viruses are maintained in complex life cycles that include an arthropod vector and a vertebrate host. The dynamics of the life cycle include the infection and transmission among animals other than humans (i.e.

non-human primates) in sylvatic/rural areas within their so-called zoonotic cycle, also known as the primary cycle (Vasilakis & Gubler, 2016). Some viruses can escape this primary cycle and establish a secondary cycle within a new vector and a new host. Epidemic events may occur as

“spillovers” from the primary cycle or permanently if the virus adapts to a secondary cycle using humans as an amplificatory host (urban cycle). Among the known arboviruses that have adapted to humans and therefore caused infections are yellow fever virus (YFV), dengue virus (DENV), Zika virus (ZIKV), Japanese encephalitis virus (JEV), West Nile virus (WNV), chikungunya virus (CHIKV), Venezuelan equine encephalitis virus (VEEV), Mayaro virus (MAYV) and Oropouche virus (OV). They cause a broad spectrum of diseases that range from asymptomatic infection to severe or fatal disease. The clinical features often show either systemic febrile illness, hemorrhagic fever or invasive neurological disease (Gubler, 2001). Globally, arboviral infections are the most common cause of morbidity. The burden caused by DENV alone (Flaviviridae family) has been estimated to be 390 million infected individuals per year worldwide (Bhatt et al., 2013), making it the most prevalent arboviral disease in tropical and subtropical countries. Nonethe- less, other (re-)emergent viruses, such as CHIKV (Togaviridae family) and ZIKV (Flaviviridae family), have affected millions of people in the Americas during the last five years (Perkins et al., 2016; Weaver & Lecuit, 2015; Fauci et al., 2016).

Although worldwide spread, most arboviruses often show a focal distribution (Vasilakis & Gubler, 2016) due to the ecological distribution of the host/vector required for effective transmission (i.e. the zoonotic cycle). In some cases, such as for YFV, despite that host/vector requirements are available for continued transmission among humans (urban cycle) the viral transmission remains focal. This is partly due to the herd immunity maintained by vaccination campaigns against YFV preventing major expansion of the virus. However, the virus could be introduced by travelers into areas with large unvaccinated populations where the vector is widely spread causing major epidemics (Monath et al., 2016; Wilder-Smith et al., 2017).

Increased travel globalized traffic and trade, anthropogenic environmental change propelled by human population growth (i.e. deforestation) and climate changes are driving local outbreaks and global spread of arboviruses (Jones et al., 2008; Wolfe et al., 2007). The rapid and continued emergence of arboviral diseases: i) pose a high burden in public health given the high morbidity observed during epidemic periods, and ii) denote a serious challenge for disease control.

These major challenges have been documented in the Americas where the increased overlap

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in the geographic distribution of arboviruses and the ample distribution of the mosquito vec-

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tors (mainly Aedes aegypti) have allowed both sequential and simultaneous outbreaks of DENV, CHIK, and ZIKV (Kraemer et al., 2015; Grubaugh et al., 2018; Vogels et al., 2019). In this context, simultaneous epidemics of arboviral diseases are challenging for public health authorities as they often have: i) a comparable epidemiologic background, ii) similar attack rates, iii) analo- gous seasonality, and iv) similar clinical symptoms, which often hinder the diagnosis and virus discrimination. Particularly, laboratory diagnosis may be complex with current methodologies as serology (cross-reaction between different arboviruses may occur), and (real-time, multiplex) reverse-transcriptase (RT-)PCR may not include all possible arboviruses and/or variants or lack sensitivity (see also below). Moreover, additional diagnostic complications are the occurrence of concurrent arboviral infections within the same host (Zambrano et al., 2016; Acevedo et al., 2017; Carrillo-Hernandez et al., 2018).

In Venezuela, arboviral diseases affecting humans such as, dengue, chikungunya and Zika have depicted sequential and/or concurrent epidemics as observed in other Latin American countries. The first DENV epidemic was reported in 1828 (Dominici, 1946) to later apparently disap- pear and re-emerge in 1964 (PAHO, 1979; Barrera et al., 2002; Uzcategui et al., 2003) becoming endemic in the country. In 2014, CHIKV was introduced in Venezuela causing a major epidemic that devastated the country (Oletta, 2015; Lizarazo et al., 2019). Two years later the third ar- boviral introduction, ZIKV, caused large outbreaks in the Americas and Venezuela which were associated to birth defects during pregnancy (Johansson et al., 2016; Cauchemez et al., 2016).

Additionally, other arboviruses with the potential to cause epidemics, such as YFV and MAYV, are present in the country. YFV circulates in Venezuela within its enzootic cycle with sporadic epizootic/epidemic cycles affecting rural areas (Auguste et al., 2015). WNV has been detected in the past among resident birds (Bosch et al., 2007) indicating the establishment of the virus. Fur- thermore, serological studies revealed a family cluster of MAYV infection in Venezuela in 2004, demonstrating its transmission to a secondary host (Torres et al., 2004) and in 2010 an outbreak of this virus was reported (Auguste et al., 2015). To improve the surveillance and diagnosis of ar- boviruses, an unbiased approach for detection of current and future arboviruses is key. This will also allow the detection of co-infections and may be helpful for disease treatment. In this thesis, we focused on both an endemic arbovirus, i.e. DENV, and an emergent arbovirus, i.e., CHIKV to understand the development of epidemics, and propose novel techniques to improve diagnosis and molecular characterization of arboviruses.

VIRUSES STUDIED IN THIS THESIS DENGUE VIRUS

DENV is an enveloped virus from the family Flaviviridae, containing a single-stranded, positive sense RNA genome with a single ORF flanked by 5’ and 3’ untranslated regions (UTRs). The genome has a type I cap (m⁷GpppAmN) that helps to stabilize the structure of the viral RNA and initiates its translation (Daffis et al., 2010) and lacks a polyadenylate tail (Wengler et al., 1978). The genome of approximately 10.7 kb encodes for three structural proteins: the capsid [C], pre-membrane [prM] and envelope [E] proteins, and seven non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, NS5) (Leitmeyer et al., 1999) (Figure 1) involved in viral RNA synthesis and protein processing (Lindenbach et al., 2013).

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DENV comprises 4 antigenically distinct serotypes (DENV-1 to 4) that share around 65% of their genome (Guzman et al., 2010). DENV show high mutation rates in their genome resulting from the error-prone RNA polymerase that yields approximately one error per round of genome replication (Drake, 1993; Holmes and Twiddy, 2003). Each serotype encompasses several genotypes: DENV-1 and DENV-3 comprise each five genotypes (I-V); while DENV-2 includes six genotypes (Asian I, Asian II, Cosmopolitan, American, American/Asian and Sylvatic); and DENV-4 contains four (I-III and Sylvatic) (Azhar et al., 2015). The genetic variability among genotypes play an important role in virulence and transmission since some genotypes are associated with increased viremia (Vaughn et al., 2000). Changes in the distribution and circulation of genotypes can lead to replacement of less virulent genotypes by genotypes frequently associated with severe disease. An example of this was the replacement of the American DENV-2 genotype with the more virulent Asian-American DENV-2 genotype (Ric- co-Hesse et al., 1997).

Figure 1. Graphic representation of the dengue virus genome organization.

CHIKUNGUNYA

CHIKV belongs to the Alphavirus genus of the Togaviridae family. The CHIKV genome consists of a positive-sense single-stranded RNA virus of ca. 12 kb. The genome has two open reading frames (ORFs): the 5´ORF, translated from genomic RNA, encoding the nsP1, nsP2, nsP3, and nsP4 non-structural proteins, and the 3´ORF, translated from sub-genomic RNA, encoding a poly- protein that is processed into the structural capsid [C], envelope [E1 and E2], proteins and two peptides [E3 and 6K] (Figure 2; Schwartz & Albert, 2010; Silva & Dermody, 2017; da Cunha &

Trinta, 2017). The virus has evolved into three major genotypes: The West African, East/Cen- tral/South African (ECSA) and Asian genotype (Powers & Logue 2007).

Figure 2. Graphic representation of the chikungunya genome organization.

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TRANSMISSION CYCLE OF DENGUE AND CHIKUNGUNYA

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Arboviruses such as CHIKV and DENV are maintained in natural (sylvatic ecosystems) transmission cycles between wild non-human primates and hematophagous arthropods (mosquitoes) (Figure 3A). These viruses often cause morbidity when there is spillover transmission to humans (Figure 3B), after which the virus could return to its sylvatic cycle again (Figure 3C).

Spillover transmission from the zoonotic cycle to humans is usually how arboviruses reach human communities. However, CHIKV and DENV may also evolve into their secondary cycle ensuing transmission between humans and mosquitoes in urban centers (Figure 3D). Interestingly, CHIKV is the only known alphavirus that is capable of using humans as amplification hosts for transmission similar to the flaviviruses DENV and ZIKV, resulting in major epidemics in the last decade (Weaver et al., 2009; Johansson et al., 2014).

The DENV sylvatic enzootic cycle involves non-human primates and arboreal mosquito species from the genus Aedes in the forest of Africa and Asia, and its endemic urban cycle involves humans with the main vector Aedes aegypti. The latter, known to be a domestic mosquito, is highly competent for DENV transmission, while Aedes albopictus has shown lower competence and is more susceptible to changes in temperature (Liu et al., 2017). Both mosquitoes are vectors of other arboviruses as well (Gublerand and Kuno 1997; Scott and Morison, 2010; Higa, 2011).

On the other hand, in its enzootic cycle, CHIKV circulates in two genetically distinct, enzootic, sylvatic transmission cycles in the forests of West Africa and the ECSA (Weaver et al., 2012). This sylvatic cycle of CHIKV involves non-human primates with the virus being transmitted by an ample range of forest-dwelling (sylvatic) Aedes spp. mosquito vectors (Figure 3). Sylvatic CHIKV periodically spills over to humans to cause individual infections and small outbreaks in Africa.

Eventually, CHIKV developed a human-endemic cycle maintained by the anthrophilic mosquitoes Aedes aegypti and Aedes albopictus (Althouse et al., 2018). Likewise, both Aedes species transmit CHIKV within the urban (human) cycle across Asia, the Indian Ocean and the Americas (Wolfe et al., 2001; Chevillon et al., 2008; Higgs et al., 2015; Stapleford et al., 2016; Shiferaw et al., 2015).

Figure 3. Transmission cycle of DENV and CHIKV. The enzootic (right) and epidemic (left) transmis- sion cycles. A) enzootic cycle, B) spillover infections to humans, C) spillback from urban cycles to initiate arboreal, enzootic cycles and D) introductions into the urban cycle. Adapted from: S.C. Weaver, N.L. Forrester / Antiviral Research 120 (2015) 32–39.

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EPIDEMIOLOGY OF THE VIRUSES DENGUE VIRUS

Dengue is currently the most important arboviral disease affecting humans (Yacoub & Farrar, 2014) with half of the world’s population living at risk of acquiring this infection (Gubler, 2011;

Murray et al., 2013; Kyle & Harris, 2008) (Figure 4). Dengue is distributed across 128 countries in Asia, the Pacific, America, Africa and the Caribbean, with recent (re-)introductions in Europe (La Ruche et al., 2010; Lourenco & Recker, 2014; Venturi et al., 2017). DENV are transmitted in urban and peri-urban settings by infected females of the day-biting mosquitoes, Aedes aegypti and Aedes albopictus (Patterson et al., 2016) although the main vector worldwide is Aedes ae- gypti. The risk of contracting dengue infection has increased dramatically since the 1940s (Ebi &

Nealon, 2016). Factors implicated in this rise are poverty, population growth, living in crowded conditions (Velasco et al., 2014; Vincenti et al., 2017), uncontrolled urbanization, lack of sanita- tion, deteriorating public health (Vasilakis & Gubler, 2016), ineffective mosquito control, as well as improved surveillance and official reporting of dengue cases. As mentioned, dengue fever is caused by any of four closely related serotypes and numerous genotypes. Infection with one serotype does not fully protect against the others, and successive infections increase the risk of developing severe dengue disease. It is estimated that only 25% of the cases show symptomatic disease while the rest progress as mild or inapparent infections

CHIKUNGUNYA VIRUS

CHIKV was first isolated in 1953 in the coastal plateaus of Mawia, Makonde and Rondo, Tanzania (Robinson, 1955; Lumsden, 1955). Since then, several episodes of urban transmission have been reported in Africa (McIntosh et al., 1963; Saluzzo et al., 1980; Thonno et al., 1999) and Asia. In 2004, a major large-scale chikungunya outbreak took place in Kenya, later reaching La Réunion Island in 2005, and subsequently spreading to several islands in the Indian Ocean and India (Caglioti et al., 2013). In Europe, the virus was first reported in 2007 causing two outbreaks in Italy (in 2007 and 2017) and sporadic autochthonous cases in Italy, Croatia, France Spain and Portugal (Rezza et al., 2007; Grandadam et al., 2011; Tomasello & Schlagenhauf, 2013; Delisle et al., 2015; Fernandez-Garcia et al., 2016; Venturi et al., 2017; Barzon 2018) ever since. In the Americas, the virus emerged in late 2013 causing a major epidemic in the region. CHIKV local infection was first reported in the island of Saint Martin in the Caribbean rapidly spreading to and causing epidemics in 45 countries and territories in the Caribbean, Central America, South America, and North America (Weaver & Forrester, 2015) (Figure 4).

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Figure 4. Global distribution of dengue, chikungunya and Zika. Countries and territories where cases have been reported until May 2017. Source: Rückert et al., Nature Communications. 8, 15412 (2017).

SPATIAL EPIDEMIOLOGY AND ARBOVIRAL DISEASES

Spatial epidemiology describes how the temporal dynamics of host, vector and virus populations interact spatially within an environment to enable pathogen transmission (Reissen, 2010). Con- sequently, spatial epidemiology adds more factors and levels to what is studied by descriptive epidemiology (time, place, people, environment, climate, among others). The inclusion of these extra layers of information is relevant in arboviral diseases because the effective transmission of dengue or chikungunya viruses require the co-occurrence in space and time of: a) a pathogen, b) a susceptible vector (e.g. Aedes aegypti) and c) a susceptible host. Since the distribution of these three populations is non-random due to ecological and socio-ecological factors that vary in space and time, the transmission of mosquito-borne pathogens is highly heterogeneous (Kitron, 1998; Ostfeld et al., 2005). On the other hand, a location where overabundance of a disease event has occurred is called a hot spot (Ord & Getis 2001). Detecting hot spots is a first step towards understanding underlying processes that generates such atypical spatial patterns (Trisayn &

Boots, 2008) and the factors that govern such patterns as well as those that determine the rate of disease spread (Vazquez-Prokopec et al., 2016; Vincenti-Gonzalez et al., 2017). In practice, the use of spatial epidemiology tools to model the spatial heterogeneity of arboviral diseases allows the detection of patterns and trends of viral diffusion and persistence. This ultimately makes it possible to identify places at high risk for arboviral transmission and correlate this with vari- ables of different nature that may be enhancing disease transmission (Eisen et al., 2009). The goal of mapping, spatial epidemiology and cluster identification activities is to reduce disease burden by generating information enabling the design of activities and policies that aid public health authorities to allocate limited resources for prevention, surveillance and control in a more cost-effective manner (Eisen & Eisen, 2011).

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CLINICAL PICTURE OF THE DISEASE DENGUE

Dengue is caused in humans by any of the four virus serotypes. All are widely distributed across the world and capable of producing the whole range of disease from asymptomatic or inapparent dengue to the most severe forms of the disease (severe dengue). Symptoms usually begin 4-7 days after an infected mosquito bite and typically last 3-10 days (Halstead, 2008). The disease is usually self-limiting with three phases through the course of DENV infection (Figure 5). Clinical presentation includes sudden onset of fever, headache, retro-orbital pain, anorexia, rash, nausea, myalgia, arthralgia, petechiae (Halstead, 1980; Harris et al., 2000;) lasting around 15 days, with a small proportion of patients progressing to severe disease, which may result in death.

Figure 5. Clinical course of DENV infection. Symptoms of infection usually begin 4-7 days after the bite of an infected mosquito, when the virus is also detectable. The febrile phase lasts 4-6 days. Viremia usually decreases after the third day of symptoms onset. The critical phase follows the febrile phase and the patient condition can improve or worsen during this period. Source: Yacoub et al., Nature Reviews Cardiology. 11, 335–345 (2014)

Timely and accurate diagnosis of dengue is important to start prompt treatment and patient management. Proper diagnosis is key to discriminate between dengue and other arboviral or febrile diseases that may have overlapping clinical symptoms. Indeed, patients with severe dengue require meticulous follow-up and clinical management in a hospital whereas patients with uncomplicated dengue can be managed on an outpatient basis (Lee et al., 2012). Such decisions are also influenced by the case classification used.

Symptomatic dengue infections were historically grouped into three categories: undifferenti- ated fever, dengue fever (DF) and dengue hemorrhagic fever (DHF). DHF was further classified into four severity grades, with grades III and IV being defined as dengue shock syndrome (DSS) (WHO 1997). However, the classification was difficult to apply in clinical settings (Guha-Sapir &

Schimmer, 2005; Deen J et al., 2006; Rigau-Perez J, 2006; Bandyopadhyay et al., 2006) leading to the implementation of a new classification system (WHO, 2009). This classification defines criteria for severe dengue (Figure 6) and classifies patients with non-severe dengue into two subgroups, a) patients with warning signs and b) those without them. Nevertheless, dengue pa-

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tients without warning signs may still develop severe dengue.

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Figure 6. Suggested dengue case classification and levels of severity dengue. Source: WHO, 2009 guidelines for diagnosis, treatment, prevention and control.

CHIKUNGUNYA

Chikungunya is a febrile illness resembling dengue in its acute phase. After an incubation period typically ranging from 3-7 days, viremia occurs and symptoms develop (Figure 7), which are generally self-limiting (Patterson et al., 2016). Most infected individuals (72-93%) develop a symptomatic disease characterized by fever, rash, arthritis and incapacitating arthralgia that in an important proportion of patients, progresses to chronic long-lasting relapsing or lingering rheumatic disease (Caglioti et al., 2013; Marimoutou et al., 2015; Elsinga et al., 2017). Arthralgia is the hallmark symptom in 87-98% of the patients and appears mainly in the ankles, wrists, the phalanges and some large joints like shoulders, elbows and knees. Rash is found in 40-50% of the cases, especially in the extremities, thorax and face (Thiberville et al., 2013).

Given the similarity in clinical presentation between dengue and chikungunya (fever, muscle pain, headache, fatigue), the latter can be misdiagnosed in areas where dengue is common. Nev- ertheless, some remarkable clinical manifestations of CHIKV infection, which include a sudden fever onset, maculopapular rash and a very debilitating joint pain (usually in the ankles, wrists, and fingers), aid to the differential clinical diagnosis between DENV and CHIKV. A high percent- age of patients enter the chronic phase of the disease manifested by persistent polyarthritis / polyarthralgia lasting more than 3 years after disease onset (Moro et al., 2012; Loreto-Horcada et al., 2014).

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Figure 7. Clinical course of chikungunya infection. Following transmission after a mosquito bite, in- fected individuals experience an acute onset of disease 2–4 days after infection. Disease onset coin- cides with rising viral titers. Patients successfully clear the virus approximately 1 week after infection,

~30%-60% of individuals experience long-term sequalae that include arthralgia, arthritis, fatigue and mental disorders. Adapted from Schwartz, 2010 Nature Reviews.

PAST AND FUTURE DIAGNOSTICS AND TYPING OF ARBOVIRUSES

The last decade has shown a change in the epidemiological landscape of arboviral diseases in the Americas that goes from the introduction of chikungunya in 2013-2014 and Zika during 2015-2016 to the continuous circulation of dengue, West Nile, yellow fever, and other arboviruses in the region. This co-occurrence of arboviruses has added another layer of complexity to the screening process and the requirements in the diagnostic laboratories of different public health settings for detection of multiple viruses. Historically, the diagnosis of arboviral diseases such as dengue, chikungunya and Zika was performed by serological tests (on post-febrile blood samples from patients), and/or isolation of the virus or detection of viral nucleic acid through molecular methods (on acute febrile patient’s samples). However, the serologic approach has become a challenge after the Zika epidemic which emerged in 2015, due to the high cross-reactivity of anti-Zika virus antibodies with other flavivirus antibodies. The E protein-based ELISA (enzyme-linked immunosorbent assay) and neutralization assays encounter difficulties to dis- tinguish between specific flavivirus infections (Lustig et al., 2018).

Protocols for detection of nucleic acids from single viruses in clinical samples using PCR (Poly- merase chain reaction) do exists, but the requirement of multiple oligonucleotides and protocols complicates the implementation of a rapid screening for several viruses (Lanciotti et al., 1992;

Pfeffer et al., 2002; Wang et al., 2016; Faye et al., 2008; Hadfiel et al., 2001; Vazquez et al., 2016).

Alternatively, detection of nucleic acids of multiple viruses (i.e. dengue, chikungunya and Zika) by multiplex, real-time reverse-transcriptase (qRT)-PCR can be used as a diagnostic tool (CDC, 2017; Santiago et al., 2018). However, this technique may not be sensitive enough, especially when referring to the low viral concentrations of ZIKV infections, resulting in the need to in- crease the volume of input material used (Santiago et al., 2018). Furthermore, proper diagnosis and surveillance require typing and tracking of new or re-emergent viral strains using sequencing-based technologies. Sanger sequencing is an often-used method to obtain targeted gene

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segments that are informative to establish genetic relationships (Anderson & Schrijver 2010)

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and/or to design proper qRT-PCR assays. Although this is a fast method, it gives partial information with limited resolution. Consequently, this approach is slowly being replaced by the use of whole-genome sequencing (WGS) which collects data from the complete genome rather than single genes. This allows typing at the highest resolution and thereby a better understanding of the dynamics of virus evolution and its implications on disease development (Rodriguez-Roche et al., 2016). Currently, a step forward in this field is the introduction of next generation se- quencing (NGS), also called deep or high-throughput sequencing, for diagnosing and monitoring infectious diseases caused by both bacteria (Deurenberg et al., 2017) and viruses (Casadella and Paredes, 2017; Hoper et al., 2016; Ramamurthy et al., 2017). Within this field, shotgun metag- enomics sequencing, i.e., non-targeted sequencing of all the nucleic acids in a clinical sample without culturing, promises to be a powerful tool. This method has been used for the discovery and detection of new viruses while studying environmental samples.

Shotgun metagenomics is highly valuable when studying viral populations because theoretical- ly, allows the detection of all variants of a virus. Furthermore, shotgun metagenomics can help the understanding of multiple virus interactions with each other and with their host including monitoring host immune responses (Graf et al., 2016; Schlaberg et al., 2017). This is particular- ly relevant in tropical and subtropical countries, where the major burden of viral morbidity is due to the multiple and simultaneous circulation of several arboviruses. Thus, applying shotgun metagenomics in such settings could be an improvement in the current workflow by adding layers of information without the requirement of specific oligonucleotides design, as required for qRT-PCR, and having the possibility of processing multiplex samples to speed up screening processes during outbreaks and surveillance efforts.

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SCOPE OF THE THESIS

Arboviral diseases of clinical and public health importance such as dengue and yellow fever, have been endemic in Venezuela during the past decades. In the last 4 years, two new arboviruses transmitted by the same vector mosquito, i.e., chikungunya and Zika, have entered the country and spread in an explosive manner. We used chikungunya to describe and model the introduction and spread of an arboviral disease into a naïve population, such as that in Venezu- ela in 2014 (Chapter 2). We also investigated the observed concomitant epidemics of dengue and chikungunya that took place at that time and tackled the question of differential diagnosis based on their clinical presentation (Chapter 3). Given the difficulty to differentiate these viral diseases only on clinical presentation, we applied an unbiased shotgun metagenomics approach and bioinformatic analyses to obtain whole genome sequences of dengue viruses directly from clinical samples, a method that will aid in diagnosis, outbreak investigation and epidemiological analysis (Chapters 4 & 6). Additionally, despite the high burden imposed by dengue in the coun- try, molecular surveillance is lacking for the last ten years and no information of recent dengue outbreaks is available. Using the methods developed in Chapters 4 & 6, we studied the molecular epidemiology of dengue viruses in Venezuela and analyzed the viral dynamics of dengue and evolutionary trends during several epidemics (Chapters 5 & 7). The findings of this thesis contribute to the understanding of arboviral dynamics and the improvement of surveillance and control of arboviral diseases in Venezuela and the American region.

In Chapter 2, we characterized the spatial dynamics of the introduction of CHIKV in 2014 into a naïve population in Venezuela. By describing and quantifying the spatial and temporal events following the introduction of chikungunya in the northern region of Venezuela we aimed to gain insight into the spatial distribution and speed of the disease spread at both global and local scale.

To depict the general spatial trend of chikungunya cases during the epidemic, a Trend Surface Analysis (TSA) was used, whereas to predict the local spatial distribution pattern of diseases cases, the kriging interpolation method was applied. Clustering of chikungunya cases across the study area allowed to describe the heterogeneous pattern in space and time.

In Chapter 3, we further investigated the development of the 2014 chikungunya outbreak in the context of a concomitant epidemic of dengue disease. We aimed to describe the epidemiological, clinical manifestations and potential risk factors of chikungunya during the 2014 epidemic in Carabobo state, Venezuela. Likewise, we compared the dynamic of the overlapping epidemic with dengue. To achieve this, we: i) characterized the chikungunya confirmed cases in Venezuela, ii) classified the unconfirmed cases based on a proposed clinical criterion, iii) compared the clinical manifestations of chikungunya and dengue cases in the north central region of Venezuela.

Both dengue and chikungunya are transmitted by the same vector, thus we witnessed a concomitant epidemic in 2014. The question on how to perform a differential diagnosis was tackled by analyzing their clinical presentation. However, better diagnostic tools are needed. Therefore, in Chapter 4, we used a metagenomics approach for dengue (as a model for arboviruses) to im- prove diagnosis, which can also be used to characterize the population diversity of arboviruses.

Specifically, we investigated the applicability of a shotgun metagenomics approach and bioinfor- matics analyses to genotype DENV directly from clinical samples without any specific amplification, and deliberate on the scalability and cost effectiveness of the approach. This method could aid in diagnosing, surveillance and outbreak management of dengue and other arboviruses.

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In Chapter 5, we reported two newly DENV-2 sequences obtained through the shotgun metage- nomics approach developed in Chapter 4 in order to update the genetic information of current circulating strains that have not been reported since 2008. This will help to follow-up the molecular epidemiology of dengue viruses in Venezuela.

The advances in NGS technologies have created a strong need for bioinformatic tools easy to be implemented into the clinical microbiology and public health surveillance routine without the need for extensive bioinformatic knowledge and infrastructure. Therefore, in Chapter 6, we de- veloped DE-NIM, an automated workflow enabling the analysis of metagenomic sequencing data for identification, serotyping, genotyping, and phylogenetic analysis of dengue viruses.

In Chapter 7, using the methods implemented in Chapters 4, we studied the molecular epidemi- ology of DENV in Venezuela and analyzed the viral dynamics of dengue and evolutionary trends during several epidemics. To this end, we used four distinct datasets with newly and historical dengue genome sequences representing the four DENV serotypes. Each dataset was used for phylogeny estimation using a Bayesian framework implemented in BEAST v1.8.4. Additional- ly, selection pressure was assessed with several methods implemented in HiPhy. Furthermore, from data obtained through a deep-sequencing approach we studied the frequency of genetic variants of dengue and its association with the patients’ clinical outcome.

Finally, in Chapter 8, I have summarized the results and discussed the most relevant findings of this thesis and discussed the future role of NGS platforms such as Illumina short read sequencing and Nanopore long read sequencing for arboviral surveillance and outbreak investigation.

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Spatial Dynamics of Chikungunya Virus, Venezuela, 2014

E. Lizarazo*

M.F. Vincenti-Gonzalez * M.E. Grillet S. Bethencourt O. Diaz N. Ojeda H. Ochoa M.A. Rangel A. Tami

*These authors contributed equally to this work

Emerg Infect Dis. 2019;25(4):672-680.

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ABSTRACT

Since chikungunya virus emerged in the Caribbean region in late 2013, around 45 countries have experienced chikungunya outbreaks. We describe and quantify the spatial and temporal events following the introduction and propagation of chikungunya into an immunological naïve population from the urban north-central region of Venezuela during 2014. The epidemic curve (n=810 cases) unraveled within five months with an R₀ = 3.7 and a radial spread traveled distance of 9.4 Km at a mean velocity of 82.9 m/day. The highest disease diffusion speed occurred during the first 90 days, while space and space-time modeling suggest that the epidemic followed a particular geographical pathway with spatio-temporal aggregation. The directionality and heterogeneity of transmission during the first introduction of chikungunya, indicated existence of areas of diffusion and elevated risk of disease occurrence and highlight the importance of epidemic preparedness. This knowledge will help manage future threats of new or emerging arboviruses.

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INTRODUCTION

Chikungunya, a reemerging mosquito-borne viral infection, is responsible for one of the most explosive epidemics in the Western hemisphere in recent years. Since its introduction in the Caribbean region at the end of 2013, chikungunya virus (CHIKV) rapidly expanded within a year to most countries of South, Central and North America (1,2). CHIKV belongs to the genus Alphavirus (Togaviridae) first isolated in Tanzania during 1952 (3). The sylvatic (enzootic) cycle of CHIKV in Africa involves non-human primates with the virus being transmitted by an ample range of forest-dwelling Aedes spp. mosquitoes (4). Within the urban (human) cycle across Asia, the Indian Ocean and the Americas, CHIKV is transmitted by Aedes aegypti and Ae. albopictus (5-7). Most infected individuals (72-93%) develop symptomatic disease characterized by fever, rash and incapacitating arthralgia progressing in an important proportion of patients to chronic long-lasting relapsing or lingering rheumatic disease (8,9). The lack of population immunity to chikungunya in the Americas alongside the ubiquitous occurrence of competent Ae. aegypti and human mobility may explain the rapid expansion of CHIKV across the continent with monthly doubling of cases during the epidemic exponential phase (10,11). At the end of 2014, more than 1 million suspected and confirmed cases, including severe cases and deaths, were reported in 45 countries and territories while this figure reached almost 3 million cases by mid-2016 (12).

Likely, the real number of cases is higher due to misdiagnosis with dengue and underreporting.

In Venezuela, the first official imported case was reported in June 2014 with local transmission soon following. Chikungunya quickly spread causing a large national epidemic affecting the most populated urban areas of northern Venezuela where dengue transmission is high. Given the paucity of official national data, epidemiological inference was used to estimate the number of cases. Although nationally the disease attack rate was estimated between 6.9 % and 13.8 % (13), the observed attack rate in populated urban areas was around 40-50% comparable to those reported in Dominican Republic (14), Asia and higher than those in La Reunion (15,16).

The rapid expansion and worldwide spread in the last decade make CHIKV one of the most public health-relevant arboviruses (17). With the (re)-emergence of other arboviruses, new large-scale outbreaks in the near future seem likely (18). Understanding and quantifying the introduction and propagation range in space and time of the initial epidemic wave of CHIKV within the complex urban settings of Latin America will shed light on arboviral transmission dynamics.

This knowledge will help manage future threats of new or emerging arboviruses operating under similar epidemiological dynamics. This study characterized the epidemic wave of CHIKV in a region highly affected by the 2014 outbreak in Venezuela. To this end, we i) described the spatial progression of the epidemic using Geographical Information Systems (GIS), ii) quantified the global geographic path that CHIKV most likely followed during the first six months of the epidemic by fitting a polynomial regression model (trend surface analysis), iii) determined the general direction and speed of the propagation wave of the disease, and finally iv) identified the local spatial-time disease clusters through spatial statistics.

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MATERIALS AND METHODS STUDY AREA:

Carabobo State, is situated in the north-central region of Venezuela (Figure 1) and is one of the most densely populated regions (19).

Figure 1. Area of study on the spatial dynamics of chikungunya virus, Venezuela, 2014. A) Venezuela. B) Carabobo State (2014 population: 2,415,506 inhabitants). C) Parishes of Carabobo State. The grading of color blue depicts the population per parish up to 2014. Most persons live in the capital city of Valencia (892,530 inhabitants); within the metropolitan area, poorer settlements are located mainly in the southern area, and the most organized and urbanized medium- and high-level neighborhoods are situated toward the north-central part.

STUDY DESIGN AND DATA COLLECTION:

A retrospective study of patient and epidemiological data collected through the national Notifiable Diseases Surveillance System (NDSS) was performed to understand the spatio-temporal spread of the 2014 chikungunya epidemic at a local and global scale. A total of 810 patients of all ages were diagnosed as suspected chikungunya-infected cases by their physicians and were reported via the NDSS to the epidemiological department of the Regional Ministry of Health (INSALUD) of Carabobo State. Patients suspected of chikungunya were those presenting with fever of sudden appearance, rash and joint pain with or without other flu-like symptoms. Patients who attended public or private health care centers across Carabobo State municipalities were included in this study. Patient data was obtained for the period between June 10th and December 3rd 2014 (epidemiological weeks [EW] 22-49) coinciding with the Venezuelan chikungunya outbreak. Data corresponding to the first visit of the patients to a healthcare center was included and comprised patient address, clinical manifestations and epidemiological risk factors. The information was entered in a database, checked for consistency and analyzed anonymously. The index case (IC) was defined as the first chikungunya patient reported by the NDSS within this region.