Climate, inter-serotype competition and arboviral interactions shape dengue dynamics in Thailand

Abstract

The incidence and global spread of dengue are reaching alarming levels. Thailand represents a critical disease epicenter and demands an understanding of the environmental and evolutionary pressures that sustain DENV transmission. Unlike most affected countries experiencing recurrent outbreaks of the same serotype or replacement of one serotype for another, Thailand is an ecological niche for all four serotypes. Favorable climate and mosquito vector availability maintain a landscape defined by stable, endemic circulation of genotypes, with minimal genetic variation attributed to sporadic, external introductions. This equilibrium is achieved through inter-serotype competition, characterized by reproductive fitness levels that maintain infections (Re>1) and elevated evolutionary rates ( ~ 10^-4), which steadily increase the genetic diversity of each serotype. This conclusion is reinforced by the identification of numerous positively selected mutations, skewed in the direction of non-structural proteins conferring replication and transmission advantages versus those present in structural proteins evading neutralizing antibodies. Precipitous drops in DENV cases following outbreaks of Chikungunya suggest that interactions with other arboviruses also impact DENV dynamics through vector competition, replication inhibition or partial cross-protection. Thailand is a major exporter of DENV cases and novel emergent lineages gaining fitness here are likely to spread internationally. Surveillance is therefore paramount to monitor diversification trends and take measures to avoid the establishment of similar sustained, local transmission in other countries.

Fig. 1. Dynamics of dengue virus cases and mosquito-viral suitability Index (Index P), in Thailand 2014-2023.

A Climatic variables (precipitation, humidity and temperature) per month obtained for Thailand from 2014 to 2023 were used as priors to estimate index. B Temporal trends and climatic influences on dengue virus transmission represented by monthly reported cases and fatalities associated with DENV in Thailand, alongside with a mosquito-viral suitability index (Index P). Index P integrates climatic data, anthropogenic factors, and the prevalence of the primary vector, Aedes aegypti. This index is computed monthly and averaged nationally, reflecting the combined impact of environmental and human variables on vector capacity and virus transmission (for detailed methodology see Materials and Methods and Supplementary Fig. S1). C Spatiotemporal patterns of mosquito-viral suitability across Thailand showed by the average monthly values of Index P from 2014 to 2023 elucidating the geographical variation in vector suitability, highlighting regions and times of heightened risk. For detailed monthly/year maps see animations provided in Supplementary Video 1. D Spatiotemporal distribution of DENV cases. Delineation of temporal dynamics of DENV transmission, identifying the geographic distribution of the cases per season considering the onset of the transmission in January, an initial peak in March, the highest peak in September, and the season’s end in December (determined in panel A). The monthly mapped cases are available in Supplementary Video 2. E Synthesis of case trajectories place alongside with population density, offering insights into the spatial clustering of outbreaks, red arrows illustrate the directional progression of the cases over the specified period. The directional progression of cases were derived from statistically determined central positions (centroids) of case distributions using kernel density estimation (KDE) across successive time periods.

Fig. 2. Phylodynamics of dengue virus (DENV) in Thailand.

A Phylogenetic reconstruction of dengue Virus. Maximum likelihood (ML) phylogenetic tree, constructed from all sequences in Dataset A (see Materials and Methods). The tree illustrates the presence of all four DENV serotypes (DENV1-4) in Thailand, revealing patterns of diversification and cluster formation that suggest endemic circulation. Each serotype is denoted in the tree, and sequences from both previous studies and the current study are highlighted. B Serotype distribution and prevalence over time. This panel displays the distribution and frequency of the four dengue serotypes in Thailand from 1980 to 2023. It highlights the temporal shifts in serotype dominance and prevalence, reflecting evolving transmission dynamics, vector-host interactions, and population immunity levels. C–F Time-stamped phylogenies represented by MCC-tree for all four DENV serotypes. Sequences from Thailand are highlighting with red tips. Genotypes within each serotype that include sequences from Thailand are denoted (the distribution of all genotypes within each serotype please see Supplementary Material Fig. S1). Insets show detailed views of the endemic lineages in Thailand across the years to highlight the different evolutionary trajectories. Lineages that didn’t share the same Thai ancestor were treated as distinct, and sequences without further diversification were considered external introductions (confirmed in Fig. 4).

Fig. 3. Dynamics of positive selection across dengue virus serotypes circulating in Thailand.

Selection dynamics within the coding regions of four dengue virus (DENV) serotypes (A) DENV1, B DENV2, C DENV3, and D DENV4 as obtained from the time-stamped phylogenies. Each panel displays a Maximum Clade Credibility (MCC) tree at the center, trees were obtained from the coding regions of each DENV serotype (see Materials and Methods for details). The left panel highlights pervasive positively selected sites for each serotype, identified using the FUBAR method (see Supplementary Data 1–13). Each positively selected site is represented by a unique color, with varying shades indicating different amino acid replacements. These sites are integrated into the time-stamped phylogenies to trace the timing of each selection event accurately. The right panel shows episodically selected sites determined by the MEME method, with sites mapped onto specific branches that were identified using the aBSREL method for detecting branch-specific selection (see Supplementary Data 1–13). Sites that persisted throughout the evolutionary history are marked with arrows. Additionally, the locations of the viral proteins are also indicated on the trees (see Supplementary Data 14, mapping). Strains within branches identified under episodic positive selection are represented with the red dots across all the temporal trees (see Supplementary Data 1–13). (*Branches belonged to the same node therefore there is not a clear evolutionary direction).

Fig. 4. Discrete phylogeographic analysis and Markov-jump trajectories of dengue virus (DENV) serotypes.

A Phylogeographic Relationships displayed using Maximum Clade Credibility (MCC) trees, this analysis elucidates the endemic diversification of DENV in Thailand and reveal the trajectories of all four DENV serotypes across the globe. The time-scaled phylogenies display ancestral nodes and current geographic locations (tips) as discrete states, illustrating the spatial and temporal spread of the virus. B Dynamic Pathways of Geographical Movement displayed as circular plots generated by the ‘circlize’ package in R to depict the Markov-jump trajectories of DENV movement. The visualization highlights the frequency and routes of viral importation, exportation, and intra-country dispersal, providing the role of Thailand the global dispersal of DENV. (Color legend for all the countries is provided at the bottom of the Figure).

Fig. 5. Demographic history of DENV lineages in a comparative analysis of chikungunya (CHIKV) and zika virus (ZIKV) transmission and the impact of CHIKV on Estimated force of infection for dengue virus.

A Demographic reconstruction using skygrid analysis. The demographic history of the six identified DENV lineages in Thailand are shown. The major epidemic waves of Chikungunya virus (CHIKV) are marked with dashed orange lines, providing context for the temporal overlay of viral expansions. B Comparative analysis of chikungunya and zika virus transmission. Temporal distributions of Chikungunya (CHIKV) and Zika virus (ZIKV) cases are also shown illustrating concurrent outbreaks with DENV (in background gray). C Spatiotemporal distribution of CHIKV Cases. Similar framework as for DENV were represented the transmission dynamics of CHIKV, with seasonal patterns beginning in January, peaking in March and September, and concluding in December. The comprehensive spatial analysis for CHIKV is available in Supplementary Video 3) D Force of infection (FOI) of DENV obtained from the random forest model compared with and without the incorporation of the CHKV cases contrasted against all cases for the different clinical categories of DENV. The drop in the cases by 2021 is denoted with a red dashed line. E Application of the selected model (incorporating the CHKV cases to forecast the dynamics of dengue virus infection determined by the estimated FOI for DENV over time, for the year 2024. The year 2021 is marked with a red dashed line.